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• Published: 27 August 2019

Comprehensive analysis of landslide stability and related countermeasures: a case study of the Lanmuxi landslide in China

• Zheng Han 1 , 2 ,
• Yange Li 1 , 2 ,
• Yangfan Ma 1 ,
• Weidong Wang 1 , 3 &
• Guangqi Chen 4  

Scientific Reports volume  9 , Article number:  12407 ( 2019 ) Cite this article

12 Citations

Metrics details

• Geomorphology
• Natural hazards

We report on a comprehensive method for analyzing landslide stability and the mitigation effect of countermeasures in this paper. The proposed method is a combination of theoretical method and numerical method. To address the uncertainties of the soil strength parameters, the rational values of these parameters are comprehensively determined by the back-analysis result of the reliability method and the result by the quantitative method, as well as the in-situ geological test. To evaluate the slope stability, the limit analysis using the 2D upper bound method and the FEM simulation using strength reduction method are performed, respectively. In order to illustrate the presented method, the so-called Lanmuxi landslide in China is selected as a case study. Results demonstrated that the stress and strain majorly concentrated at the toe and crown of the slope. According to the analysis results, countermeasures consisting of anchor lattice beams, landslide piles, and cracks filling, are suggested to reduce the failure risk of the landslide. Effect assessment based on the FEM analysis verifies the feasibility and effectiveness of the recommended countermeasures.

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Introduction.

Landslides are a common geological phenomenon in mountainous regions worldwide, posing a severe risk to local infrastructures. During the years from 2004 to 2010, 2620 fatal landslides in total were recorded, causing 32,322 fatalities 1 . To protect residents and infrastructures against landslides, a rational design of countermeasures is necessary. The commonly used countermeasures include supporting measures and drainage measures 2 . Current studies in landslide hazard mitigation mainly focus on the overall design principles, structure optimization, and the mitigation effect evaluation. However, it is widely accepted that an effective countermeasure against landslide depends on the deep understanding of landslide mechanisms and rational analysis of landslide stability, which remain a scientific challenge.

Up-to-date studies on landslides can be briefly summarized in several categories, e.g., landslide mechanisms 3 , 4 , 5 , failure behaviour simulation 6 , 7 , 8 , 9 , sensitivity analysis 10 , 11 , 12 , as well as countermeasure design and optimization 13 , 14 . These studies have underlaid a solid theoretical foundation for landslide mitigation work.

The studies on the landslide mechanisms provide a fundamental understanding of the landslide process. Previous studies 15 , 16 , 17 have long proposed various failure models of landslides. These remarkable studies also substantiated the key factors dominating the landslide process, e.g., topography, material strength 3 , 18 , as well as some other triggering factors, e.g., rainfall precipitation and earthquakes 19 , 20 . These studies explore important features of landslide process and benefit the practical work.

Most studies regarding landslide mechanisms are commonly empirically or semi-empirically based. However, a physical and quantitative analysis of the landslide process is required, because slope stability, as well as magnitude and possible runout extension are the key parameters to the countermeasure design. In this context, some attempts have been made to evaluate slope stability. In order to reproduce the landslides process, it is essential to determine the key parameters of landslides, cohesion strength c , and internal friction angle φ . The two parameters are mainly based on the in-situ test, engineering experience analogy, and back analysis 21 at present. Being an efficient solution to determine the key parameters, the back-analysis method can be briefly summarized into two categories, the deterministic method 22 , 23 and reliability method 24 , 25 , 26 , 27 , 28 . Previous studies 22 also support that the back-analysis provides reliable results approximating the expected shear strength parameters.

In order to estimate the potential risk of slope failure, the potential magnitude and runout extension should be estimated. It is a major issue and a complex task for landslide mitigation work. Numerical simulation provides an alternative solution for this purpose. Several models have been developed and applied to practical work. They use the discontinuous deformation analysis (DDA) method 29 , 30 , 31 , the finite element method (FEM) 32 , 33 , the discrete element method (DEM) 34 , 35 , the smoothed particle hydrodynamics method (SPH) 7 , 9 , to analyse the stability and simulate the failure process of the landslide. Some other studies also incorporate hydraulically based models to simulate landslide behaviour, such as DDA-SPH coupled method 36 and shallow water assumption-based model 37 . However, difficulties remain in the measurement of related parameters in these numerical models. Owing to the significant individual differences from case to case, as well as the temporal and spatial variation, some important parameters are difficult to determine, requiring trial-and-error adjusting during simulation 38 . In this sense, a comprehensive analysis of landslide stability and failure process simulation remains a major topic.

In this paper, we report on a comprehensive method for analysing landslide stability and its failure process. Two essential issues are discussed, i.e., comprehensive estimation of soil strength parameters using the back-analysis results using the reliability method, and the evaluation of the slope stability using the limit analysis and the FEM simulation. The so-called Lanmuxi landslide in China is selected as a case study to illustrate the presented method.

Back analysis of key parameters

In this paper, we first use reliability theory to back analyse the shearing strength parameters. Back analysis is an effective method for determining landslide parameters. For landslide mitigation work, the safety factor F s is commonly used as an indicator for the stability. The slope is likely to fail when F s  ≤ 1, while is under a limit state when F s  = 1. In the reliability method, the function equation is incorporated to represent the limit state of the slope. The function is as below,

where X 1 , X 2 , X 3 , …, X n denotes random variables regarding to slope stability. In our study, only two parameters, the cohesion c and friction angle φ are considered and assumed as random variables. Slope stability can be quantitated by the value of Z . Z  < 0 denotes that the slope is at risk of failure, while Z  = 0 represents that the slope stays in the limit state. When Z  > 0, the slope keeps stable. Another key parameter in reliability theory is the slope reliability index β , which refers to the probability of a slope that completes the pre-determined function under the specified condition and time. Based on previous studies 39 , the reliability index β can be expressed as:

where μ i denotes the averaged value, and σ i represents the standard deviation of the variable. The failure model is established based on the limit state (Z = 0) in the back-analysis of parameters using the reliability method. The final back-analysis result is determined according to the minimum reliability index β   40 , which means, the key parameters X i are iteratively solved until a minimum β is obtained.

The spreadsheet-based method 41 , 42 is used to solve the key parameters X i . The reliability index β is expressed as the forms of Hasofer-Lind’s reliability index 43 ,

where X denotes random variables, μ is the averaged value of the random variables, C x is the covariance matrix of variables, F is the failure domain. Equations ( 1 , 3 ) can be iteratively solved according to the optimization algorithm. Compared with the traditional deterministic back analysis method, reliability method generates better results because the effect of the uncertainties in the landslide parameters can be considered.

Stability analysis based on the 2D upper bound (UB) theory

Presently, the majority of slope stability analyses evaluate a safety factor using a 2D representation of the slope, while 3D analyses of slope stability are much less reported 44 , 45 . It can be explained in part by the fact that a 3D model is likely to introduce a much larger number of degrees-of-freedom, which demands significantly more computational time and effort than the 2D model 46 . Meanwhile, in the 3D model, the failure surface does not only cross weak soil layers, but also strong ones with uncertainties, consequently increases the calculated factor of safety, e.g., 14% to 18% increasement by Reyes and Parra 47 and 13.9% by Xie 48 , compared with the minimum factor of safety in the 2D model. In a word, the 2D analysis could lead to more conservative results than the 3D analysis in slope stability problems.

A primary concern in the practice of landslide mitigation is safety. For this consideration, we use the 2D upper bound (UB) limit analysis (LA) method to evaluate a conservative slope stability in this paper. The UB method is an energy-based method, using the principle of virtual work 49 , 50 . According to the virtual work equations, the internal work of the slope should be equal to its external work (i.e., D int  =  W ext ) in the limit state. A kinematically admissible velocity field 51 is presented for calculating the virtual work. The velocity formula can be described as follows,

where v i and v i −1 are the virtual velocities of a slice on the sliding surface. [ v ] i −1, i represents the relative velocity that defined as the vector difference from v i to v i −1 . It should be noticed that v i , v i −1 and [ v ] i −1, i satisfy the closure relations. φ i and φ i− 1 are the internal friction angles of the neighboured slice, respectively. [ φ ] i −1, i denotes the relative internal friction angle in the vertical direction of slices. α i and α i− 1 are the inclined angles. Therefore, the UB solution of the safety factor can be calculated by the following equation:

where φ f  = arctan(tan φ/F s ). l i is the sliding surface length of the slice i . P w and U represent the external pressure arisen by pore water. P w can be calculated by \({P}_{w}=\frac{1}{2}{\rho }_{w}{Z}_{w}\) , where P w denotes the density of water and the Z w represents the depth of pore water. U is obtained by constructing the equilibrium equation with W i and α i , at vertical direction. W i is the gravity of the slice i . [ h ] i−1, i is the length of the interface between the slice i and the slice i- 1. Equations ( 4 – 6 ) can be further explained by Fig.  1 .

The 2D stability analysis based on the UB theory. ( a ) Virtual velocity field of slope divided into vertical slices; ( b ) Pore water pressure on the sliding layer.

Stability analysis using the FEM strength reduction method

Compared with the traditional LA method, the finite element method (FEM) performs better with the advantage of considering much complex boundary conditions, as well as the non-homogeneity of the soil and rock mass. Another advantage of the FEM method is that the stress and deformation field can be also obtained. The FEM strength reduction method has been proposed and applied in the analysis of slope stability 52 , 53 . It regarded the safety factor F S as the reduction degree of shear strength of the soil material when the slope reaches the limit state. The safety factor F S can be redefined as F S  =  c / c f or F S  = tan φ /tan φ f , where c and φ are the initially-input shear strength parameters, while c f and φ f are the output shear strength parameters in the limit state after reduction, respectively.

The definition of the safety factor mentioned above is consistent with the definition introduced by Bishop 54 , that the safety factor F S is expressed as bellow,

where, τ f represents the shear strength of the slope and can be calculated by the Mohr-Coulomb model with the cohesion and internal friction angle. τ denotes the actual shear stress of slope. Equation ( 7 ) can be transformed as follow,

Equation ( 8 ) means that the slope will reach the limit state with the c ′ =  c / F s and tan φ ′ = tan φ / F s , which is the same with the definition mentioned above. Therefore, in our study, the reduction equations of shear strength are expressed as bellow, deducing with the assumption of the constant external load,

where R denotes the reduction coefficient with an initiate value of R  = 1.0. In each iteration step, R increases with the stress and deformation analysis based on FEM implemented. The Mohr-Coulomb model is introduced to describe the constitutive relationship of the landslide mass. Commonly, the vertical boundary of the model is fixed in the horizontal direction, while the bottom boundary is constrained in both horizontal and vertical directions. The iteration of simulation breaks until the slope reaches the limit state. The value of the variable R under the limit state is regarded as the safety factor Fs for the selected landslide profile.

Background of the case study

The so-called Lanmuxi landslide is located in the northwest of Xikou village, Fenghuang town, Hunan province, China (as shown in Fig.  2a ). Evidence of previous landslide had been previously observed on July 2014. Owing to the impact of continuous heavy rainfall, the slope deformed, causing cracks in the crown and middle part of the slope body. Subsequently, on September 2014, two secondary landslides were triggered due to a heavy rainstorm, resulting in new cracks occurred in the crown region of the slope, with a maximum width up towards to 0.50 m. The total length of the landslide area is approximately 100.00 m. The maximum width and average thickness of the landslide reach 206.00 m and 4.40 m, respectively. The area of this landslide is estimated 1.37 × 10 4 m 2 , with a total mass volume of 6.08 × 10 4 m 3 . The slope body and the sliding layer mainly composed of silty clay, while the substrate consists of argillaceous siltstone. Geological investigation shows that there are only tiny amounts of pore water in the stratum and no groundwater is observed.

Overview of the Lanmuxi landslide. ( a ) Location of the landslide. ( b ) Map of the Lanmuxi landslide (using Grapher® 10, https://www.goldensoftware.com/products/grapher ). ( c ) Photographic view (Photograph was taken by Z. Han).

The in-situ investigation after the secondary landslide event illustrates that the slope is still unstable, threatening 134 residents and 272 local buildings, with a total potential economic loss of 1.8 million dollars. The overview of the Lanmuxi landslide is shown in Fig.  2b,c .

Back analysis of the shearing strength parameters

In order to describe the state of the landslide under diverse condition reasonably, two different conditions are considered. In Condition 1, the mass of the slope is dry, while in Condition 2, it is supposed to be saturated under the impact of heavy rainfall. The saturated soil mass of the slope increases self-weight and consequently reduces shearing strength, leading to a greater risk for landslide failure.

According to the laboratory test on the three groups of soil material samples that obtained in-situ , the mean values of the parameters in this case are listed in Table  1 . In the back analysis using the UB theory and the reliability method, the standard deviations are required. The laboratory test shows that the standard deviations of the shear strength parameters in the sliding layer are ± 2.98 kPa and ± 1.04° in the Condition 1, while ± 3.02 kPa and ± 1.96° in the Condition 2. The back analysis based on the deterministic method and reliability method are conducted, respectively. Results are shown in Table  2 .

Laboratory test and back analysis are comprehensively considered to obtain the parameter of the sliding layer. We use the laboratory test results in Table  1 and the back-analysis results in Table  2 to attain the averaged values of the parameters in Table  3 , which are suggested as the best-fitting parameters for stability analysis and FEM simulation.

Stability analysis

As mentioned in the above section, it has been widely accepted that the 2D analysis could lead to more conservative results than the 3D analysis in slope stability problems. For the safety consideration, we evaluate a conservative slope stability using a 2D model. To simplify the actual 3D slope into a 2D model, the length along the plane direction, i.e., C1-C1′ and D1-D1′ in Fig.  2b is hereafter referred to as the two typical slope profiles. Most of the local buildings and infrastructures are distributed at the landslide toes along the directions of the selected profiles. The slopes along the both profiles are presumed to be infinitely wide in the 2D model, negating the 3D effects caused by the infinite width of the sliding mass.

Owing to the significant reduction of the safety factor when soil mass is saturated, we mainly focus on the stability analysis under Condition 2. Two analysis methods, the UB method and the FEM strength reduction method, are conducted, respectively. The results in Table  4 illustrate that the landslide along Profile 2 is unstable ( F s  < 1).

Stress and deformation analysis using the FEM method

In order to perform stress and deformation analysis using the FEM method, the both profiles are meshed into triangle blocks (Fig.  3 ). Profile 1 is meshed into 1494 blocks with 2515 nodes, while Profile 2 is separated into 1209 blocks with 2029 nodes. In each iteration step of the FEM simulation, stress and deformation are calculated and landslide strength is gradually reduced. Iteration breaks until the slope section comes to the limit state.

Finite element mesh. ( a ) Profile 1 (C1-C1′). ( b ) Profile 2 (D1-D1′).

Figure  4 and 5 illustrate the stress and deformation distribution. Figure  4a–d demonstrate the distribution of shear stress, effective stress, strain, and shear strain of Profile 1. While the plastic strain, the deformation along both horizontal and vertical directions under the limit state are shown in Fig.  4e–g . In contrast, Fig.  5 only shows the limit state of Profile 2, because the slope along this profile is unstable when soil mass is saturated, limiting the FEM simulation converged. As such, we only use the Eq. ( 9 ) to calculate the plastic strain and deformations of this profile in the limit state. Different from the strength reduction method which increases the reduction coefficient R as illustrated in Eq. ( 9 ), in order to obtain a converged FEM simulation results, we use a decreased R in each iteration step in the analysis of Profile 2. The shear strength consequently increases in each step until the unstable slope along Profile 2 reaches a limit state. Figure  4 indicates that the stress and deformation of Profile 1 majorly concentrate at the toe, while Fig.  5 reveals that the stress and strain of Profile 2 in the limit state majorly concentrate at the crown.

The FEM analysis results of Profile 1. ( a ) Shear stress. ( b ) Effective stress. ( c ) Strain. ( d ) Shear strain. ( e ) Plastic strain (the limit state). ( f ) Deformation in x direction (the limit state). ( g ) Deformation in z direction (the limit state).

The FEM analysis results of Profile 2. ( a ) Plastic strain (the limit state) ( b ). Deformation in x direction (the limit state) ( c ). Deformation in z direction (the limit state).

Suggestion of countermeasures

The comprehensive results of stability analysis demonstrate that the slope along Profile 1 (C1-C1′) approximates the limit state, while Profile 2 (D1-D1′) is unstable. Therefore, in order to protect local buildings at the downslope area, countermeasures are required. As shown in Figs  4 and 5 , the FEM analysis indicates the weak parts of the slope along both profiles. The stress and strain majorly concentrate on the toe and main body along the Profile 1, as well as the landslide crown along the Profile 2. These weak parts are supposed to subject obvious surface deformation that up towards to 142 mm as shown in Fig.  5c . In this context, we suggest structural strengthening at these weak parts, using anchor lattice beams at the landslide body, anti-slide piles at the landslide toe, as well as intercepting drains and cracks filling at the landslide crown. The length of the anchorage section is 4 m, and the anchoring force exerted by each anchor is 500 kN. The overall configuration of the landslide countermeasures is shown in Figs  6 and 7 .

The plane layout of landslide mitigation measures (the figure was generated by Grapher 11, version 11.7.825, https://www.goldensoftware.com/products/grapher ).

The profile layout of landslide mitigation measures. ( a ) Profile 1. ( b ) Profile 2.

To evaluate the effect of the countermeasures, an effect assessment is conducted in this paper by using the 2D UB method and the FEM strength reduction method. The analysis results are shown in Fig.  8 and Table  5 . Figure  8 reveals an obvious control of the slope deformation after settling the structural strengthening. The maximum positive deformation in x direction of Profile 1 (C1-C1′) is reduced from 166.8 mm to 41.6 mm, while 162 mm to 95.2 mm of Profile 2 (D1-D1′). Table  5 shows that the safety factors of Profile 1 and 2 have been increased 43.6% and 43.8% respectively after the slope reinforcement. These results have demonstrated the feasibility and effectiveness of the recommended countermeasures.

The FEM analysis results of slope deformation in x-direction (after the slope reinforcement). ( a ) Profile 1. ( b ) Profile 2.

Limitations

For the simplification of calculation, the reliability method introduced in this study is performed with the assumption that the shear strength parameters are independent to each other. Therefore, the negative correlation between these parameters as revealed in previous studies 55 , 56 , and the impact of spatial correlation of shear strength remains unknown and were not considered in this study. The above limitation should be considered in future works.

Another limitation is with respect to the FEM simulation. Presently, effects of tension cracks are not considered in our analysis. However, previous study 57 applied the kinematic approach of limit analysis to assess the stability of uniform cohesive friction slopes with cracks, indicating that failure mechanisms departing from the crack tip can lead to a significant overestimation of the stability of the slope. For this reason, the slope profile 1 in the case study may be instable in view of many observed tension cracks in the crown. Improvement on this issue is ongoing.

The third limitation in this paper relates to the 2D simplification of the actual complex 3D slope. The composition of the slope is usually heterogeneous and, in combination with the complicated soil layers, it is commonly difficult to select appropriate profiles to analyse its stability. In the 2D limit analysis of slope stability, the slope is presumed to be infinitely wide along the out-of-plane direction, negating the 3D effects caused by the infinite width of sliding mass. In consequence, the 2D slope stability analysis may be over-conservative and insufficient. In this sense, a large and realistic 3D model should be built and analysed in order to overcome such apparent instability found in the 2D analysis.

In this paper, we use a comprehensive analysis method to evaluate the stability of the Lanmuxi landslide and discuss the related countermeasures. The presented method has advantages in considering the uncertainties of the soil shear strength parameters, and generating more conservative results compared to the deterministic method.

The rational values of the sliding layer parameters are comprehensively determined by using the mean value of the back-analysis result of the reliability method, as well as the results by the deterministic method and the in-situ test. Following the determination of parameters, the 2D UB method of limit analysis and the FEM strength reduction method are performed for stability evaluation. The safety factors Fs along two typical profiles of the slope are calculated, indicating that the slopes along both profiles are unstable. The FEM-based analysis furthermore demonstrates the weak parts of the slope where stress and strain concentrate.

Structural countermeasures, using anchor lattice beams, landslide piles, and cracks filling, are suggested based on the comprehensive analysis results. Subsequently, an effect assessment based on UB method and FEM simulation is implemented. Results show notable decreasing of the deformation and about 40% increasing of the safety factors, which demonstrates the feasibility and effectiveness of the recommended countermeasures.

Petley, D. Global patterns of loss of life from landslides. Geology. 40 (10), 927–930 (2012).

Article   ADS   Google Scholar  

Ma, N., Ma, H. M. & Feng, W. Q. Interaction between countermeasure works and landsliding mass: a case study. The 10th Asian Regional Conference of IAEG. 1–6 (2015).

Leroueil, S. Natural slopes and cuts: movement and failure mechanisms. Geotechique. 51 (3), 197–243 (2001).

Article   Google Scholar  

Take, W. A., Bolton, M. D., Wong, P. C. P. & Yeung, F. J. Evaluation of landslide triggering mechanisms in model fill slopes. Landslides. 1 (3), 173–184 (2004).

Wang, F. W., Sassa, K. & Wang, G. Mechanism of a long-runout landslide triggered by the august 1998 heavy rainfall in Fukushima prefecture, Japan. Engineering Geology. 63 (1), 169–185 (2002).

Wu, J. H. Seismic landslide simulations in discontinuous deformation analysis. Computers & Geotechnics. 37 (5), 594–601 (2010).

Article   CAS   Google Scholar  

Pastor, M., Haddad, B., Sorbino, G., Cuomo, S. & Drempetic, V. A depth‐integrated, coupled SPH model for flow‐like landslides and related phenomena. International Journal for Numerical & Analytical Methods in Geomechanics. 33 (2), 143–172 (2010).

Article   ADS   MATH   Google Scholar  

Yuan, R. M., Tang, C. L., Hu, J. C. & Xu, X. W. Mechanism of the donghekou landslide triggered by the 2008 wenchuan earthquake revealed by discrete element modeling. Natural Hazards & Earth System Sciences. 14 (5), 3895 (2014).

Han, Z. et al . Numerical simulation of debris-flow behavior based on the SPH method incorporating the Herschel-Bulkley-Papanastasiou rheology model. Engineering Geology. 255 , 26–36 (2019).

Ashis, K. S., Ravi, P. G. & Irene, S. An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides. 2 (1), 61–69 (2005).

Catani, F., Lagomarsino, D., Segoni, S. & Tofani, V. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Natural Hazards & Earth System Sciences. 13 (11), 2815–2831 (2013).

Han, Z. et al . An integrated method for rapid estimation of the valley incision by debris flows. Engineering Geology. 232 (8), 34–45 (2018).

Zaruba, Q. & Mencl, V. Landslides and their control. Engineering Geology. 7 (3), 281–282 (1982).

Google Scholar  

Wei, Z., Li, S., Wang, J. G. & Ling, W. A dynamic comprehensive method for landslide control. Engineering Geology. 84 (1), 1–11 (2006).

Terzaghi, K. Mechanism of landslides. Application of Geology to Engineering Practice. 83–123 (1950).

Wieczorek, G.F. Chapter 4-landslide triggering mechanisms. Landslides: investigation and mitigation. (1996).

Huang, R. Q. Large-scale lan 357 dslide and their sliding mechanisms in China since the 20th century. Chinese Journal of Rock Mechanics and Engineering . 26 (3),433–454 (in Chinese) (2007).

Zhou, S. H., Chen, G. Q. & Fang, L. G. Distribution pattern of landslides triggered by the 2014 Ludian earthquake of China: implications for regional threshold topography and the seismogenic fault identification. ISPRS International Journal of Geo-information. 5 (4), 46 (2016).

Iverson, R. M. Landslide triggering by rain infiltration. Water Resources Research. 36 (7), 1897–1910 (2000).

Malamud, B. D., Turcotte, D. L., Guzzetti, F. & Reichenbach, P. Landslide inventories and their statistical properties. Earth Surface Processes and Landforms. 29 (6), 687–711 (2004).

Sonmez, H., Ulusay, R. & Gokeceoglu, C. A practical procedure for the back analysis of slope failures in closely jointed rock masses. Int J Rock Mech Mining Sci. 35 (2), 219–233 (1998).

Tang, W. H., Stark, T. D. & Angulo, M. Reliability in back analysis of slope failures. Soils Found. 39 (5), 73–80 (1999).

Wang, L., Hwang, J. H., Luo, Z., Jiang, C. H. & Xiao, J. Probabilistic back analysis of slope failure—a case study in Taiwan. Comput Geotech. 51 , 12–23 (2013).

Low, B. K. & Tang, W. H. Efficient reliability evaluation using spreadsheet. J Eng Mech. 123 (7), 749–752 (1997).

Low, B. K. & Tang, W. H. Reliability analysis using object-oriented constrained optimization. Struct Saf. 26 (1), 69–89 (2004).

Low, B. K. FORM, SORM, and spatial modeling in geotechnical engineering. Struct Saf. 49 , 56–64 (2014).

Zhang, J., Tang, W. H. & Zhang, L. M. Efficient probabilistic back-analysis of slope stability model parameters. J Geotech Geoenviron. 136 (1), 99–109 (2009).

Article   MathSciNet   Google Scholar  

Zhang, L. L., Zhang, J., Zhang, L. M. & Tang, W. H. Back analysis of slope failure with Markov chain Monte Carlo simulation. Comput Geotech. 37 (7), 905–912 (2010).

Yin, K. L., Jiang, Q. H. & Wang, Y. Numerical simulation 387 on the movement process of Xintan landslide by DDA method. Chinese Journal of Rock Mechanics and Engineering . 21(7), 959–962 (in Chinese) (2002).

Zhang, Y. et al . DDA validation of the mobility of earthquake-induced landslides. Engineering Geology. 194 , 1069–1075 (2015).

Wu, J. H. & Chen, C. H. Application of DDA to simulate characteristics of the Tsaoling landslide. Computers and Geotechnics. 38 (5), 741–750 (2011).

Chen, H. & Lee, C. F. A dynamic model for rainfall-induced landslides on natural slopes. Geomorphology. 51 (4), 269–288 (2003).

Crosta, G. B., Chen, H. & Lee, C. F. Replay of the 1987 Val Pola Landslide, Italian Alps. Geomorphology. 60 (1), 127–146 (2004).

Boon, C. W., Houlsby, G. T. & Utili, S. New insights into the 1963 Vajont slide using 2D and 3D distinct-element method analyses. Geotechnique. 64 (10), 800–816 (2014).

Li, X. P., He, S. M., Luo, Y. & Wu, Y. Simulation of the sliding process of Donghekou landslide triggered by the Wenchuan earthquake using a distinct element method. Environmental Earth Sciences. 65 (4), 1049–1054 (2012).

Wang, W., Chen, G., Zhang, Y., Zheng, L. & Zhang, H. Dynamic simulation of landslide dam behavior considering kinematic characteristics using a coupled DDA-SPH method. Engineering Analysis with Boundary Elements. 80 , 172–183 (2017).

Article   MathSciNet   MATH   Google Scholar  

Han, Z. et al . Numerical Simulation of Debris-flow Behavior Incorporating a Dynamic Method for Estimating the Entrainment. Engineering Geology. 190 , 52–64 (2015).

Han, Z. et al . Numerical simulation for run-out extent of debris flows using an improved cellular automaton model. Bulletin of Engineering Geology & the Environment. 76 (3), 961–974 (2017).

Verma, A. K., Srividya, A. & Karanki, D. R. Reliability and safety engineering. Springer, London. (2010).

Sun, L. C., Wang, H. X. & Zhou, N. Q. Application of reliability theory to back analysis of rocky slope wedge failure. Chin J Rock. Mech Eng. 31 (A01), 2660–2667 (2012).

Low, B. K. Reliability analysis of rock slopes involving correlated nonnormals. Int J Rock Mech Mining Sci. 44 (6), 922–933 (2007).

Zhao, L. H., Zuo, S., Lin, Y. L. & Zhang, Y. B. Reliability back analysis of shear strength parameters of landslide with three-dimensional upper bound limit analysis theory. Landslides. 13 (4), 711–724 (2016).

Lü, Q. & Low, B. K. Probabilistic analysis of underground rock excavations using response surface method and SORM. Comput Geotech. 38 (8), 1008–1021 (2011).

Hicks, M. A., Nuttall, J. D. & Chen, J. Influence of heterogeneity on 3D slope reliability and failure consequence. Computers and Geotechnics. 61 , 198–208 (2014).

Xiao, T., Li, D. Q., Cao, Z. J. & Zhang, L. M. CPT-based probabilistic characterization of three-dimensional spatial variability using MLE. Journal of Geotechnical and Geoenvironmental Engineering. 144 (5), 04018023 (2018).

Liu, Y. et al . Probabilistic stability analyses of undrained slopes by 3D random fields and finite element methods. Geoscience Frontiers. 9 , 1657–1664 (2018).

Reyes, A. & Parra, D. 3D slope stability analysis by the using limit equilibrium method analysis of a mine waste dump. In: Proceedings Tailings and Mine Waste 2014, Keystone, Colorado, USA, October 5–8 (2014).

Xie, M. W., Wang, Z. F., Liu, X. Y. & Xu, B. Three-dimensional critical slip surface locating and slope stability assessment for lava lobe of Unzen volcano. Journal of Rock Mechanics and Geotechnical Engineering. 3 (1), 82–89 (2011).

Chen, W. F. Limit analysis and soil plasticity. Elsevier Scientific Pub. Co. (1975).

Chen, W. F. Limit analysis in soil mechanics. Elsevier Scientific Pub. Co. (1990).

Michalowski, R. L. Slope stability analysis: a kinematical approach. Geotechnique. 45 (2), 283–293 (1995).

Zienkiewicz, O. C., Humpheson, C. & Lewis, R. W. Associated and non-associated visco-plasticity in soil mechanics. Geotechnique. 25 (4), 671–689 (1975).

Duncan, J. M. State of the art: limit equilibrium and finite element analysis of slopes. discussion and closure. Journal of Geotechnical Engineering. 123 (9), 577–596 (1996).

Bishop, A. W. The use of the slip circle in the stability analysis of slopes. Geotechnique. 5 (1), 7–17 (1955).

Cherubini, C. Reliability evaluation of shallow foundation bearing capacity on c′, φ′ soils. Can Geotech J. 37 (1), 264–269 (2000).

Wolff, T. F. Analysis and design of embankment dam slope: a probabilistic approach. University Microfilms. (1985).

Utili, S. Investigation by limit analysis on the stability of slopes with cracks. Geotechnique. 63 (2), 140–154 (2013).

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Acknowledgements

This study was financially supported by the National Key R&D Program of China (Grant No. 2018YFC1505401, Z. Han); the National Natural Science Foundation of China (Grant No. 41702310, Z. Han); the Natural Science Foundation of Hunan (Grant No. 2018JJ3644, Z. Han); the Innovation-Driven Project of Central South University (Grant No. 2019CX011); and the Fundamental Research Funds for the Central Universities of Central South University. These financial supports are gratefully acknowledged.

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Zheng Han, Bin Su, Yange Li, Yangfan Ma & Weidong Wang

State Key Laboratory of Geohazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, Sichuan, 610059, China

Zheng Han & Yange Li

Key Laboratory of Heavy-haul Railway, Ministry of Education, Changsha, Hunan, 410075, China

Weidong Wang

Department of Civil and Structural Engineering, Kyushu University, 819-0395, Fukuoka, Japan

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Z.H., Y.G.L. and B.S. designed the study. Y.F.M. and W.D.W. performed the in-situ investigation. G.Q.C. simulates the performance of the suggested countermeasures. Z.H. and B.S. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Han, Z., Su, B., Li, Y. et al. Comprehensive analysis of landslide stability and related countermeasures: a case study of the Lanmuxi landslide in China. Sci Rep 9 , 12407 (2019). https://doi.org/10.1038/s41598-019-48934-3

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• Unit 4: Anatomy of a tragic slide: Oso Landslide case study

Sarah Hall (College of the Atlantic) Becca Walker (Mt. San Antonio College)

This activity was selected for the On the Cutting Edge Reviewed Teaching Collection

This activity has received positive reviews in a peer review process involving five review categories. The five categories included in the process are

For more information about the peer review process itself, please see https://serc.carleton.edu/teachearth/activity_review.html .

Landslides can have profound societal consequences, such as did the slide that occurred near Oso, Washington in 2014. Forty-three people were killed and entire rural neighborhood was destroyed. In this unit, students consider the larger-scale tectonic and climatic setting for the landslide and subsequently use lidar and SRTM (Shuttle Radar Topography Mission) hillshade images, topographic maps, and InSAR (interferometric synthetic aperture radar) to determine relationships between landscape characteristics and different types of mass-wasting events. They conclude by considering the societal costs of such a disaster and ways that communities in similar situations may mitigate their risk.

Expand for more detail and links to related resources

Activity Classification and Connections to Related Resources Collapse

Grade level.

View Standards Details »

Science and Engineering Practices

Obtaining, Evaluating, and Communicating Information: Compare, integrate and evaluate sources of information presented in different media or formats (e.g., visually, quantitatively) as well as in words in order to address a scientific question or solve a problem. HS-P8.2:

Constructing Explanations and Designing Solutions: Apply scientific ideas, principles, and/or evidence to provide an explanation of phenomena and solve design problems, taking into account possible unanticipated effects. HS-P6.3:

Analyzing and Interpreting Data: Analyze data using tools, technologies, and/or models (e.g., computational, mathematical) in order to make valid and reliable scientific claims or determine an optimal design solution. HS-P4.1:

Cross Cutting Concepts

Stability and Change: Change and rates of change can be quantified and modeled over very short or very long periods of time. Some system changes are irreversible. HS-C7.2:

Patterns: Empirical evidence is needed to identify patterns. HS-C1.5:

Cause and effect: Cause and effect relationships can be suggested and predicted for complex natural and human designed systems by examining what is known about smaller scale mechanisms within the system. HS-C2.2:

Disciplinary Core Ideas

The Roles of Water in Earth's Surface Processes: The abundance of liquid water on Earth’s surface and its unique combination of physical and chemical properties are central to the planet’s dynamics. These properties include water’s exceptional capacity to absorb, store, and release large amounts of energy, transmit sunlight, expand upon freezing, dissolve and transport materials, and lower the viscosities and melting points of rocks. HS-ESS2.C1:

Plate Tectonics and Large-Scale System Interactions: Plate tectonics is the unifying theory that explains the past and current movements of the rocks at Earth’s surface and provides a framework for understanding its geologic history. Plate movements are responsible for most continental and ocean-floor features and for the distribution of most rocks and minerals within Earth’s crust. HS-ESS2.B2:

Performance Expectations

Earth and Human Activity: Construct an explanation based on evidence for how the availability of natural resources, occurrence of natural hazards, and changes in climate have influenced human activity. HS-ESS3-1:

Learning Goals

Unit 4 learning outcomes × div[id^='image-'] {position:static}div[id^='image-'] div.hover{position:static} a view from a washington army national guard helicopter shows trees that were at the very top of the hill when the mudslide occurred now lie across the slide's width like a woven belt. provenance: photo by sgt. matthew sissel joint forces headquarters, washington national guard https://www.dvidshub.net/image/1209685/oso-mudslide reuse: this item is in the public domain and maybe reused freely without restriction..

• Students will identify several environmental characteristics of a natural landscape that increase an area's mass-wasting susceptibility.
• Students will create a map with appropriate symbology illustrating the morphological characteristics of a mass-wasting event.
• Students will use geodetic data to compare the appearance, existence, and morphology of surface features before and after a mass-wasting event.

Unit 4 Teaching Objectives

• Cognitive: Provide opportunities for students to use climate and geodetic data to characterize the evolution of landscape features after a mass-wasting event has occurred.
• Behavioral: Facilitate student ability to qualitatively assess and map the changes in geomorphic features before and after a mass-wasting event.
• Affective: Facilitate student ability to analyze the societal impact of mass-wasting events.

Context for Use

The content in Unit 4 is appropriate for introductory geology, hazards, and other geoscience courses; sophomore-level courses in which geodesy and/or geomorphology/surface processes are being introduced; or non-geoscience courses where infrastructure planning, Earth hazards, and/or the nature and methods of science are being investigated. Unit 4 activities can be adapted to serve small or large-enrollment classes and can be executed in lecture and lab settings as an in-class activity in which students work in small groups, a lab exercise, or as part of a ~2 week investigation of the use of geodesy to understand surface-process hazards and decision making using the entire Surface Process Hazards module.

This unit precedes Unit 5: Mitigating future disasters—developing a mass-wasting hazard map in which students create a hazard map and report on an area susceptible to mass wasting. If the entire two-week module will not be used, we recommend pairing Unit 4 with Unit 5 to give students the opportunity to use geodetic data to identify landscape features associated with a slow-slip mass-wasting event and classify low, medium, and high-risk areas based on landscape characteristics. However, Unit 4 does build on skills from Unit 2: Reading the landscape and Unit 3: Understanding landslide factors so you may need to have to give some background practice or information on these topics.

Description and Teaching Materials

Part 1: preparatory activity.

• In each of the five study sites that students have looked at in Units 2 and 3, there is a significant mass wastage somewhere in the map area. In order to prepare for Unit 4, have students prepare five-minute presentations on one those mass-wastage events (except the Northern Washington one, as the rest of Unit 4 is about the Oso Landslide in that region). If you have a large class, you may want to have the students work in groups or randomly select one person/team from each of the four sites to present. This process helps students understand more about the societal cost of mass wasting and the different types of ones that can occur.
• Unit 4 preparation student exercise (Microsoft Word 2007 (.docx) 304kB Jun20 17)
• USGS: History of Landslides at the Base of Bare Mountain, Tully Valley, Onondaga County, New York Report
• USGS: Landslide Susceptibility in the Tully Valley Area, Finger Lakes Region, New York
• USGS: Report of the Thistle Slide
• Utah Geological Survey: Geosights: Thistle Landslide Revisited
• USGS: Rockslide-debris Avalanche of May18, 1980, Mt St Helens Volcano
• USGS: Mt St Helens 1980 Cataclysmic Eruption
• AK Dept of Fish & Game: [link https://pubs.er.usgs.gov/publication/70168586 Part 2: Setting the stage 'A submarine landslide source for the devastating 1964 Chenega tsunami, southern Alaska']
• Earthquake, Landslide and Tsunami Hazards in the Port Valdez area, Alaska

In order to place the Oso Landslide in a larger geologic context, students will review the environmental and built characteristics of the northwest Washington locality using the maps from Units 2 and 3.

A) Plate tectonic setting Seismic and volcanic hazards exist along all of eastern Washington state related to the subduction of the Juan de Fuca Plate beneath the North American Plate. This is a good opportunity to relate the plate tectonic setting to the landscape features (faults, linear ridges/streams, mountains) and present hazards. Use the same map-set for Northern Washington as you did in Unit 3. Northern Washington Maps Unit 3-4 (Acrobat (PDF) 41.3MB Oct22 20)

We suggest one of the following already-existing plate tectonics activities:

• GPS Data and Earthquake Hazards in Cascadia - Students learn to read GPS timeseries to calculate velocities and then analyze what the changing velocity across Cascadia means for earthquake hazard.
• Analyzing Plate Motion Using EarthScope GPS Data - Student use a spreadsheet of GPS data from the Plate Boundary Observatory (PBO) to measure the motion of GPS stations in the Pacific Northwest. They generate and map annual velocity vectors of GPS stations.

B) Climatic setting Washington state has an excellent example of high-precipitation region and a rain shadow. High seasonal precipitation focused on the western slopes of the Cascades and arid conditions prevailing over eastern Washington. In this activity students looks at climate data from near Oso, Washington, as well as a precipitation map of Washington to learn more about the general climate of the area as well as in the time leading up to the slide itself.

Washington precipitation map (Acrobat (PDF) 1.2MB Feb25 16) Unit 4: Oso Washington climate assessment student exercise (Microsoft Word 2007 (.docx) 426kB Jun20 17) Unit 4: Oso Washington climate assessment student exercise PDF (Acrobat (PDF) 361kB Aug28 17) Unit 4: Oso Washington climate assessment student exercise answer key -- private instructor-only file Hide Unit 4: Oso Washington climate assessment student exercise answer key This file is only accessible to verified educators. If you are a teacher or faculty member and would like access to this file please enter your email address to be verified as belonging to an educator. Email Adress Submit Unit 4: Oso Washington climate assessment instructor calculations -- private instructor-only file Hide Unit 4: Oso Washington climate assessment instructor calculations This file is only accessible to verified educators. If you are a teacher or faculty member and would like access to this file please enter your email address to be verified as belonging to an educator. Email Adress Submit

Part 3. Mass-wasting anatomy—Oso, WA, Case Study

Along with the assignment below and the map set they have already used, students should be given a lidar hillshade image onto which they will identify, map, and label morphological features of the mass-wasting event. They will answer qualitative questions and will color code their maps to show regions of high/moderate/low risk for damage due to slip. Students will then be shown spatial data of the region at various timesteps (including SRTM [Shuttle Radar Topography Mission], lidar, and InSAR maps of the locality) and answer questions related to the geology and societal impacts of the event.

• Northern Washington Maps Unit 3-4 (Acrobat (PDF) 41.3MB Oct22 20)
• Unit 4 Oso Landslide Exercise (Microsoft Word 2007 (.docx) 34MB Oct21 20) Unit 4 Oso landslide exercise PDF (Acrobat (PDF) 16.4MB Oct21 20)

This file is only accessible to verified educators. If you are a teacher or faculty member and would like access to this file please enter your email address to be verified as belonging to an educator. Email Adress Submit

Teaching Notes and Tips

• This unit seems to go forward in a pretty straightforward way. Although it takes a little extra time, we really do recommend the brief student presentations in Part 1. If students have never done PowerPoint presentations (or other presentation software), you may need to spend a little time explaining what to do or give guidance on what each slide should contain.

Formative Assessment

Most likely, formative assessment will be gathered through discussion with students and observation of students as they work through the exercise. By monitoring classroom activity, the instructor can gauge how students are progressing toward the learning outcomes.

Summative Assessment

The primary summative assessment for the module is the Oso Landslide Exercise. In addition, in order to assess whether students have achieved a sufficient understanding of the societal side of the unit, one of the following questions could be used on a test.

Level 2: Besides the mass of material that buried the town, the Oso mass-wasting event left a mark on the geographic and societal landscape both locally and regionally. Students will be asked to answer the following questions:

• Name and describe two examples of a local indirect effect of the Oso mass-wasting event—near to the town of Oso.
• Name and describe two examples of a regional indirect effect of the Oso mass-wasting event—downstream of the event?

Level 3: Students will research what has been done to plan for the future in the aftermath of the Oso event. They will prepare a one-page narrative describing how the community and/or state is working to prevent or preparing for the future in this landslide-prone region.

References and Resources

• Jin-Woo Kim, Zhong Lu, Feifei Qu & Xie Hu (2015) Pre-2014 mudslides at Oso revealed by InSAR and multi-source DEM analysis , Geomatics, Natural Hazards and Risk, 6:3, 184-194, DOI: 10.1080/1947705.2015.1016556
• Haugerud, R. A. (2014) Preliminary Interpretation of Pre-2014 Landslide Deposits in the Vicinity of Oso, Washington , USGS Open-File Report 2014–1065
• USGS Follow-up March 2015: One Year Later—The Oso Landslide in Washington .
• Wikipedia Oso Mudslide
• Introduction to Geodesy and Surface Process Hazards Presentation (PowerPoint 2007 (.pptx) 14.7MB Jul11 17)
• https://www.oregongeology.org/Landslide/Landslidehome.htm
• http://www.seattle.gov/dpd/aboutus/whoweare/emergencymanagement/
• http://www.portlandoregon.gov/pbem/53935
• http://landslides.usgs.gov/
• http://landslides.usgs.gov/learn/prepare.php
• http://pubs.usgs.gov/circ/1325/

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• Initial Publication Date: October 2, 2017 | Reviewed: December 10, 2020
• Short URL: https://serc.carleton.edu/189147 What's This?

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Folkestone Warren, Kent

Landslide case study

The Folkestone Warren landslide is a very large, deep-seated coastal landslide that is well known to geologists and engineers. It is about 3 km wide and up to 350 m in length. There are nine surveys in the BGS National Landslide Database : IDs 1774/1 to 1774/9.

The village of Capel-le-Ferne is situated behind the landslide at the cliff top and the wide undercliff is given over to a country park.

Folkestone Warren location map. BGS © UKRI.

The Dover to London mainline railway (via Cheriton) passes through the entire length of the Warren, with the Abbotscliff Tunnel at the eastern end and the Martello Tunnel at the western end. Since its construction, the railway has been regularly disrupted by landslide activity and considerable expenditure has been, and continues to be, involved in remediation and monitoring. The old Dover road is situated close to the cliff edge. This has also been disrupted, and re-routed in parts, for the same reason.

The geology of the platform at beach level varies along the length of the Warren. At the eastern end, the platform changes from Gault Formation to chalk. The change occurs approximately in line with the eastern end of the concrete toe-weight apron (constructed in 1953), near Horsehead Point. Rock revetment has been placed to the east of the concrete apron by Network Rail. It is notable that, westward of Horsehead Point, the platform is displaced seaward by about 50 m due to movements of the various slide masses (Trenter and Warren, 1996).

Figure 1 The Folkestone Warren Landslide (Photograph taken September 2002)

The geology consists of the White and Grey Chalk subgroups overlying the Gault Formation and the Folkestone Formation , the principal slide surface being located at the Gault/Folkestone junction.

The upper part of the chalk sequence here is the White Chalk Subgroup comprising, from top to bottom, the New Pit Chalk Formation resting on the Holywell Nodular Chalk Formation .

The lower part of the chalk sequence is the Grey Chalk Subgroup , formed by the Zig Zag Chalk Formation resting on the West Melbury Marly Chalk Formation . Superficial deposits forming the Clay-with-Flints Formation overlie the chalk.

Beneath the chalk sequence lies the Gault Formation, with the thin ‘Chloritic Marl’ bed at their junction. The Gault Formation consists of between 44 and 48 m of overconsolidated clay. Beneath the Gault Formation is the Folkestone Formation of the Lower Greensand Group , with the ‘Sulphur Band’ at their junction. The Folkestone Formation consists of calcareous and glauconitic sands and sandstones with some argillaceous horizons (Trenter and Warren, 1996).

The Folkestone Warren landslide is one of the largest on the English coast and is a classical example of a deep-seated (approximately 40 m below sea level) multiple retrogressive, compound mechanism, having translational , rotational and graben (sunken block) features.

The Folkestone Warren cliffs are divided into the chalk ‘High Cliff’, essentially the overall landslide backscarp, and the sea ‘cliff’, consisting largely of landslipped material. The High Cliff as such is not directly influenced by the sea, unlike the Folkestone Warren landslide as a whole.

The landslide has been extensively studied since 1915, when a major re-activation occurred throughout the complex seriously disrupting the railway that was constructed in 1844 (Hutchinson, 1969; Hutchinson et al., 1980; Trenter and Warren, 1996; Warren and Palmer, 2000). Included in this event were chalk slides from the High Cliff, in particular the ‘Great Fall’ landslide. Hutchinson et al. (1980) describes the three dominant subvertical joint sets within the chalk and their influence on the form of the High Cliff and on landslides within it. In recent decade,s a large amount of instrumentation and remedial works has been installed and maintained by Network Rail within the Warren.

Massive rotational failure in 1915 affecting the Folkestone to Dover train (British Railways, Southern Region).

The age of the original landslide event is not known. Most landslide movements in recent times have been confined to the undercliff in the form of re-activations of existing, deep-seated landslide masses. However, numerous significant slope failures of the High Cliff are believed to have occurred since the 1800s.

As part of a programme of work monitoring coastal erosion and landsliding at several sites around the coast of Great Britain, BGS has surveyed the Folkestone Warren landslide complex with LiDAR .

The Folkestone Warren Landslide. BGS © UKRI (taken September 2002).

Folkestone Warren cliffs. BGS © UKRI.

Folkestone Warren from railway bridge. BGS © UKRI.

Folkestone Warren landslide (east). BGS © UKRI.

More information

Hutchinson, J N. 1969. A reconsideration of the coastal landslides at Folkestone Warren, Kent. Géotechnique , Vol. 19(1), 6–38.

Hutchinson, J N, Bromhead, E N, and Lupini J F. 1980. Additional observations on the Folkestone Warren landslides . Quarterly Journal of Engineering Geology & Hydrogeology , Vol. 13(1), 1–31. DOI: https://doi.org/10.1144/GSL.QJEG.1980.013.01.01

Trenter, N A, and Warren, C D. 1996. Further investigations at the Folkestone Warren landslide. Géotechnique , Vol. 46(4), 589–620.

Warren, C D, and Palmer, M J. 2000. Observations on the nature of landslipped strata, Folkestone Warren, United Kingdom. Proceedings of the 8th International Symposium on Landslide s, Cardiff, June 2000.

Acland, C L. 1869. The late landslip on the Warren. Quarterly Journal of the Folkestone Natural History Society , Vol. 3, 48–51.

Arber, M A. 1940. The coastal landslips of south-east Devon . Proceedings of the Geologists’ Association , Vol. 51, 257–271.

Barton, M E, and McCosker, A M. 2000. Inclinometer and tiltmeter monitoring of a high chalk cliff. 127–132 in Landslides in Research, Theory and Practice . (Bromhead, E, Dixon, N, and Ibsen, M-L (editors). (London, UK: Thomas Telford.)

Bell, F G, Cripps, J C, Edmonds, C N, and Culshaw, M G. 1990. Chalk fabric and its relation to certain geotechnical properties. 187–193 in Chalk .

Bishop, C H. 1973. Folkestone: The Story of a Town. (London and Ashford, UK: Headley Brothers Ltd.)

Bromhead, E N, Hopperm A C, and Ibsen, M-L. 1998. Landslides in the Lower Greensand escarpment in south Kent . Bulletin of Engineering Geology and the Environment , Vol. 57(2), 131–144.

Bromhead, E N, and Ibsen, M-L. 2004. Bedding-controlled coastal landslides in Southeast Britain between Axmouth and the Thames Estuary . Landslides , Vol. 1, 131–141.

Drew, F. 1864. The geology of the country between Folkestone and Rye, including the whole of Romney Marsh. Memoir of the Geological Survey of Great Britain. (London, UK: HMSO.)

Folkestone Chronicle, 10 December, 1859.

Folkestone Chronicle, 11 February 1856.

Folkestone Chronicle, 20 January 1877.

Gentleman’s Magazine. 1806. Country News (June). Vol. 76, 575.

Haselock, P J, and Gale, A R. 1998. The Folkestone Warren landslip: a model for the decay of fault scarps? Proceedings of the 19th Annual Meeting of the Tectonic Studies Group .

Hutchinson, J N. 1965. A survey of coastal landslides in Kent. Building Research Station Note No 35/36.

Hutchinson, J N. 1986. Cliffs and shores in cohesive materials; geotechnical and engineering geological aspects. 1–44 in Proceedings of the Symposium on Cohesive Shores , 5–7 May 1986 Burlington, Ontario. Skafel, M G (editor).

May, V J. 1964. A study of recent coastal changes in southern England. MSc thesis (unpublished) University of Southampton.

McDakin, J G. 1900. Coast Erosion: Dover Cliffs.

McDakin, J G. 1911. Abbotscliff (Warren) Chalkfall Geological Notes. East Kent Scientific and Natural History Society Reports and Transactions , Vol. 16, 15.

McGowan, A, Roberts, A G, and Woodrow, L K R. 1988. Geotechnical and planning aspects of coastal landslides in the United Kingdom. In: Landslides Glissements de Terrain . Bonnard, C (editor). Vol. 2.

Morgenstern, N R, and Price, V E. 1965. The analysis of the stability of general slip surfaces. Géotechnique , Vol. 15, 79–93.

Muirwood, A M. 1971. Engineering aspects of coastal landslides. Proceedings of the Institution of Civil Engineers , Vol. 50, 257–276.

Osman, C W. 1917. The landslips of Folkestone Warren and thickness of the Lower Chalk and Gault near Dover. Proceedings of the Geologists’ Association , Vol. 28, 59–84.

Phillips, W. 1821. Remarks on the Chalk cliffs in the neighbourhood of Dover, and on the Blue Marls covering Green Sand near Folkestone. Transactions of the Geological Society , Vol. 5, 16–46.

Price, F G H. 1877. On the beds between the Gault and Upper Chalk near Folkestone. Quarterly Journal of the Geological Society , Vol. 33, 431–448.

The Railway Magazine. 1919. Railway landslides: engineering problems at Wembley and the Warren. July issue, 45: 23–31.

The Times, 14 December 1859, page 12.

Toms, A H. 1946. Folkestone Warren landslips: research carried out in 1939 by the Southern Railway Company. Institute of Civil Engineers , Railway Paper No. 19.

Topley, W. 1893. The landslip at Sandgate. Proceedings of the Geologists’ Association , Vol. 13(2), 40–47.

Viner-Brady, N E V. 1955. Folkestone Warren landslips: remedial measures 1948–1954 . Proceedings of the Institution of Civil Engineers, Vol. 4(3), 429–441.

Wood, M. M. (1955), Folkestone Warren landslips: investigations 1948–1950 . Proceedings of the Institution of Civil Engineers , Vol. 4(3), 410–428.

Contact the Landslide Response Team

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• Published: 15 June 2020

GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia

• Tilahun Mersha 1 , 2 &
• Matebie Meten 1  

Geoenvironmental Disasters volume  7 , Article number:  20 ( 2020 ) Cite this article

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Simada area is found in the South Gondar Zone of Amhara National Regional State and it is 780Km far from Addis Ababa. Physiographically, it is part of the northwestern highlands of Ethiopia. This area is part of the Guna Mountain which is characterized by weathered volcanic rocks, rugged morphology with deeply incised gorges, heavy rainfall and active surface processes. Many landslides have occurred on August 2018 after a period of heavy rainfall and they caused many damages to the local people. In this study, Frequency Ratio (FR) and Weights of Evidence (WoE) models were applied to evaluate the landslide causative factors and generate landslide susceptibility maps (LSMs). The landslide inventory map that consists of 576 active and passive landslide scarps was prepared from intensive fieldwork and Google Earth image interpretation. These landslide locations were randomly divided into 80% training and 20% validation datasets. Seven landslide causal factors including aspect, slope, curvature, lithology, land use, rainfall and distance to stream were combined with a training dataset using GIS tools to generate the LSMs of the study area. Then the area was divided into five landslide susceptibility zones of very low, low, moderate, high and very high. Later, the resulting maps have been validated by using area under the curve and landslide density index methods. The result showed that the predictive rate of FR and WoE models were 88.2% and 84.8%, respectively. This indicated that the LSM produced by FR model showed a better performance than that of WoE model. Finally, the LSMs produced by FR and WoE models can be used by decision-makers for land use planning and landslide mitigation purpose.

Introduction

Landslides are one of the recurrent natural problems that are widespread throughout the world, especially in mountainous areas which caused a significant injury and loss of human life, damage in properties and infrastructures (Parise and Jibson 2000 ; Dai et al. 2002 ; Glade et al. 2005 ; Kanungo et al. 2006 ; Pan et al. 2008 ; Girma et al. 2015 ). The term “landslide” is the movement of a mass of rock, debris or earth down a slope under the influence of gravity (Varnes 1978 ; Hutchinson 1989 ; WP/WLI - International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory 1990 ; Cruden 1991 ; Cruden and Varnes 1996 ). Landslides are caused by different triggering factors such as heavy or prolonged precipitation, earthquakes, rapid snow melting and a variety of anthropogenic activities. Landslides can involve flowing, sliding, toppling or falling movements and many landslides exhibit a combination of two or more types of movements (Crozier 1986 ; Cruden and Varnes 1996 ; Dikau et al. 1996 ).

Landslide in Ethiopia is a common phenomenon which often causes significant damage to people and property. Almost 60% of the total population in Ethiopia lives in the highland areas (Ayalew 1999 ) which is characterized by high relief, complex geology, high rainfall, rugged morphology, very deep valleys and gorges with active river incision. The rapid population growth demanded the use of areas which were not previously used for settlement, urban expansion, agricultural and other purposes thereby exposing these areas to landslide problems after rainy seasons (Temesgen et al. 2001 ; Abebe et al. 2010 ; Woldearegay 2013 ).

In recent years landslide incidences are increasing in the Ethiopian highlands due to man-made and natural causes (Meten et al. 2015b ). For instance, from 1960 to 2010 alone, Landslides have killed 388 people, injured 24 people, and damaged agricultural lands, houses and infrastructures (Ayalew 1999 ; Temesgen et al. 1999 ; Woldearegay 2008 and (Ibrahim: Landslide assessment and hazard zonation in Mersa and Wurgessa, North Wollo, Ethiopia, unpublished)). According to Abebe et al. ( 2010 ), the highlands and mountainous area of Ethiopia like the Blue Nile Gorge, the Lower Wabe-Shebele River valley, Gilgel Gibe River, Tarmaber, Kombolcha - Dessie road, Uba Dema village in Sawla, Wondogenet area and many other parts of Ethiopia are repeatedly facing problems associated with landslides. The landslides in these areas are affecting human lives, infrastructures, agricultural lands and the natural environment. As a result of this, the study of the landslide has drawn global attention to increase awareness about its socioeconomic impacts and the pressure of increasing population and urbanization on mountainous areas (Kanungo et al. 2006 ).

The current study area is found in Simada District of South Gondar Zone in the Amhara National Regional State of Northwestern Ethiopia. It is part of the northwestern Ethiopian highlands. This area is severely affected by landslide incidences in recent years. Landslide incidence in the study area occurred on August, 2018 after a heavy and prolonged rainfall that caused the death of animals, destruction of houses and wide areas of cultivated and non-cultivated lands. Therefore, this area requires a detailed investigation to evaluate the causes, types and failure mechanisms of landslides and to prepare the landslide susceptibility maps. A systematic landslide study helps to reduce the damages in infrastructures, houses and cultivated lands and loss of lives. This importance will be noticed when these landslide susceptibility maps are used by decision-makers in regional land use planning, landslide prevention and mitigation measures.

For proper and strategic land use planning, it is important to evaluate and delineate landslide prone areas using different landslide susceptibility mapping techniques. Preparing a landslide susceptibility map of a certain area is a useful tool in landslide hazard management as it shows the degree of susceptibility of an area to landslide occurrence. It is obvious that landslide susceptibility maps can be generated based on the assumption that future landslide will occur under the same condition as in the past (Pham et al. 2015 ). Interpretation of future landslide occurrence needs an understanding of conditions and processes that control landslides in the study area. Past landslides and different conditioning factors such as slope morphology, hydrogeology and geology of the area are the main parameters to assess and evaluate landslide susceptibility by integrating these conditioning factors and past landslides in a GIS environment.

GIS-based landslide susceptibility mapping techniques have been used by several researchers (Aleotti and Chowdhury 1999 ; Kanungo et al. 2009 ) which can be classified into qualitative and quantitative ones (Yalcin et al. 2011 ; Felicisimo et al. 2012 ; Peng et al. 2014 ; Wang and Li 2017 ). Qualitative techniques include geomorphological analyses and inventory methods. These are based on expert judgment and are more subjective than quantitative methods. Quantitative methods such as deterministic analyses, probabilistic approaches and statistical techniques closely rely on mathematical models which have much less personal bias but still needs experience to produce and run these models (Aleotti and Chowdhury 1999 ; Kanungo et al. 2009 ). In recent years, many landslide susceptibility maps were produced using GIS-based statistical approaches like Frequency Ratio (FR) and Weights of Evidence (WoE) models. This is because the result from these models showed good performance with high accuracy and these models are very simple to implement and can provide the contribution of each causative factor class for landslide occurrence (Lee and Pradhan 2007 ; Akgun et al. 2007 ; Dahal et al. 2008 ; Işık Yilmaz 2009 ; Pradhan, Lee and Buchroithner 2010 ; Choi et al. 2012 ; Park et al. 2012 ; Vakhshoori and Zare 2016 ; Fayez et al. 2018 ).

Several researchers have used Frequency ratio model on landslide studies (Bahrain et al. 2014 ; Meten et al. 2015a ; Haoyuan Hong et al. 2015 ; Pham et al. 2015 ; Pirasteh and Li 2017 ; Fayez et al. 2018 ; Khan et al. 2019 ) and in comparison with a few methods (Akgun et al. 2007 ; Lee and Pradhan 2007 ; Işık Yilmaz 2009 ; Choi et al. 2012 ; Park et al. 2012 ; Meten et al. 2015b ; Wang and Li 2017 ). A combination of both FR and WoE models have been applied for landslide susceptibility mapping (Regmi et al. 2013 ; Rahmati et al. 2016 ). Gholami et al. ( 2019 ) also compared the prediction capability of frequency ratio, fuzzy gamma and landslide index models. Each GIS-based statistical method requires data on past landslides, preparatory causative factors and triggering factors. To prevent or mitigate any damage from landslides, it is essential to assess the landslide prone areas. The current study aims to carryout landslide susceptibility mapping by applying FR and WoE models in order to highlight critically high and very high hazard zones. This will help to reduce and mitigate any hazard associated with future landslide occurrence.

The study area is 185.7 square kilometers which is located in Simada District of South Gondar Zone, Amhara National Regional State, Ethiopia (Fig. 1 ). The area is bounded between 38 0 11' E and 38 0 20' E longitudes and 11 0 30' N and 11 0 41' N latitudes. The typical drainage pattern of the study area is dendritic and parallel. Atkus and Kostet Rivers are the main rivers that affect the study area by eroding the banks of rivers leading to slope instability. The confluence of these rivers forms Bijena River which is the largest river in the study area. Most of the rivers in the study area flow towards the southeast direction. The physiography of the study area forms the rugged topography of Guna Mountain (Fig. 2 ) which is part of the northwestern Ethiopian highlands. The area can be classified into two main physiographic regions. These are the plateau area and the rugged terrain. The plateau areas are characterized by volcanic landscapes that represent the high flatlands of the Kefoye, Agona and Jinjero Gedel areas. These areas are water divide zones in which rivers are flowing to Abay Basin in the west and to Bashilo Basin in the south. In this area, the slopes are ranging from flat slopes on the top to steeper slopes at the plateau scarp. The rugged terrain is highly dissected by major rivers and streams which are characterized by deep narrow valleys and gorges. Slopes in these areas are steep to vertical and susceptible to erosional and landslide phenomena. The elevation of the study area ranges from 2067m to 3586 m which comprises of medium to very high relief hills. The presence of steep scarps, rugged slope faces, deep gorges and steep ridges showed that this area is prone to active surface processes and landslide incidences. Based on elevation, the climatic zones of the study area are mostly falling under the highland climatic zone. The primary wet season extends from June to September. There is great variation in the rainfall amounts with maximum rainfall occurring during the wet season which starts in June and ends in September with the heaviest rainfall occurring during the months of July and August.

Location map of the study area

Three dimensional map of the study area

In order to achieve the objectives of this research, data collection and organization, preparation of landslide inventory datasets, database construction of landslide causative factors and application of FR and WoE models were carried out to prepare the landslide susceptibility maps and validate them.

Data collection and organization

The necessary data for this study were collected from various sources. These include collecting relevant literatures from published and unpublished papers, DEM data from USGS, a regional geological map from Geological Survey of Ethiopia at a scale of 1:250000, rainfall data from National Metrological Agency of Ethiopia, a topographic map from Ethiopian Geospatial Information Agency at a scale of 1:50000 and Google Earth image from Google Earth. During field work, data collection was carried out on different rock types by describing their character, the relative degree of weathering, slope steepness, location of springs and swamps, landslide inventory mapping on both active landslide and scarp areas by measuring their length, width, accumulation zone and depth (if possible), land use and land cover, man-made activities including farming practice. After compilation of the actual field investigation, the data has been systematically processed and analyzed first in ArcGIS followed by Microsoft Excel and then finally in ArcGIS.

Preparation of landslide inventory dataset

The quality of the landslide inventories depends on the accuracy, type and certainty of the information shown in the maps. New and emerging mapping methods, based chiefly on satellite, aerial and terrestrial remote sensing technologies, can greatly facilitate the production and the update of landslide maps. Literature review has shown that the most promising approaches exploit VHR optical, monoscopic and stereoscopic satellite images, analyzed visually or through semi-automatic procedures, and VHR digital representations of surface topography captured by LiDAR sensors. A combination of satellite, aerial and terrestrial remote sensing data represents the optimal solution for landslide detection and mapping, in different physiographic, climatic and land cover conditions (Guzzetti et al. 2012 ). Ye et al. ( 2019 ) detected landslides from hyperspectral remote sensing data using a deep learning technique.

The landslide inventory dataset in the current study consist a total of 576 landslides which were identified from Google Earth image interpretation and intensive field survey. For landslide susceptibility mapping landslide polygons can be divided into training and validation datasets. The training dataset is used for constructing the predictive model while the validation dataset is used for validating the model. In this study, the specific date of landslide occurrence is not well known. Hence, the landslide polygons were randomly split into two classes with 80% for training and 20% for validation by keeping their spatial distribution into account (Fig. 3 and 4 ). In addition, the validation data sets for most of the landslide susceptibility or hazard assessments were chosen in between 20% and 30% of the total landslide inventory.

Flowchart of the research work (Note: LS = landslide)

Landslide inventory map showing the distribution of landslides

Database for landslide causative factors

To undertake landslide susceptibility analysis in the study area, a spatial database was first constructed for the causative factors within the spatial analysis tools of ArcGIS 10.4 software. The database consists of the landslide inventory datasets (training and validation) and the landslide causative factors (slope, aspect, curvature; land use, lithology, rainfall and distance from stream). These factors were subsequently evaluated by calculating their weights from the relationship between the landslide and landslide causative factors and then these results were verified. There are no strict rules or guidelines for the triggering factors to be used in different statistical approaches for landslide susceptibility mapping. Instead, the chosen factors should be operative and measurable depending on a particular area’s characteristics (Ayalew and Yamagishi 2005 ). One parameter may be an important controlling factor for landslide occurrence in a certain area but in most cases a combination of two or more landslide causative factors may be effective in addition to the triggering factor for landslide occurrence.

In this study, the triggering factor was heavy and prolonged rainfall. During the fieldwork, landslide locations were identified and marked with GPS, land use (land cover) types around the landslide scar, drainage networks and spring locations, lithological units and human activities were investigated to prepare the landslide susceptibility maps.

Generally, the selection of landslide causative factors should consider the nature of the study area and the availability of data. In this regard, a total of seven parameters were selected including slope, aspect, curvature, lithology, rainfall, land use and distance to stream. All causative factor maps were converted into raster maps with the same coordinate system (WGS 1984 UTM zone 37N) and the same pixel size (30mx30m). The rasterized training (80%) landslide map and all the causative factor maps have been overlaid and the information was extracted using the spatial analyst tool of ArcGIS to calculate the ratings or weights of all factor classes for FR and WoE models. The summation of these ratings or weights of each landslide factor will help to evaluate the spatial relationship between them and the probability of landslide occurrence in the study area.

Topographic parameters like slope, aspect, curvature and distance to stream maps were derived from Digital Elevation Model (DEM) with a cell size of 30 m by 30 m. Lithology and land use maps were prepared from intensive fieldwork and Google Earth image interpretations. The rainfall map was generated using IDW interpolation technique of the spatial analyst tool in ArcGIS from four rain gage stations near to the study area using the rainfall data from National Meteorological Agency of Ethiopia.

Frequency ratio (FR) model

Frequency Ratio model is a well-known and widely used bivariate statistical method that is used for landslide susceptibility mapping (Lee and Talib 2005 ; Akgun 2012 ; Demir et al. 2013 ; Mezughi et al. 2011 ; Yalcin et al. 2011 ; Abay and Barbieri 2012 ; Mondal and Maiti 2013 ; Paulin et al. 2014 ). In this model, processing the input data, computations and output-processes are very simple and can be easily understood. It is simple and relatively flexible to use and implement a landslide susceptibility map with accurate results (Lee and Pradhan 2007 ; Yilmaz 2009 ; Choi et al. 2012 ; Mohammady et al. 2012 ; Park et al. 2012 ). The frequency ratio model is one of the probabilistic models which are based on the observed relationship between the distribution of landslides and each landslide related factor (Lee and Talib 2005 ). To evaluate the contribution of each factor towards landslide susceptibility, the training landslide group was combined with thematic data layers separately and then the frequency ratio of each factor’s class was calculated according to the following procedures.

First, the number of pixels for landslide occurrence and non-occurrence in each factor’s class was calculated. Second, the percentage of each factor’s class having landslide to the total pixels containing landslide of the factor was calculated and the percentage of each factor class’s number of pixels to the total number of pixels in the study area was calculated. Finally, the frequency ratio of each factor class was obtained by dividing the percentage of landslide pixels to the percentage of area pixels in each factor classes (Equation 1 ).

Where; Npix ( S i , j ) = the number of pixels containing landslide within class j in factor i; Npix ( N i , j ) = the number of pixels of class j in factor i; ∑ j NPix ( S i , j ) is the number of total pixels containing landslide in the study area; ∑ j NPix ( N i , j ) is the number of total pixels in the study area.

The calculated FR value represents the degree of correlation between landslide and a certain class of the causative factor. A value of 1 is an average value for the landslide occurrence of a specific landslide causative factor class. A value more than 1 indicates a strong and positive correlation and a high probability of landslide occurrence, while a value of less than 1 indicates a negative relationship and low probability of landslide occurrence in a certain class of a landslide causative factor. The FR map of each causative factor is prepared with the help of ArcGIS by assigning the calculated FR values. Then the FR values of all the causative factor maps were overlaid and numerically added using the raster calculator of the spatial analyst tool in ArcGIS 10.4 to prepare the Landslide Susceptibility Index (LSI) map. LSI is computed by summing the FR values of all the landslide causative factor maps (Equation 2 ) and then the resulting LSI map was further reclassified in to very low, low, moderate, high and very high landslide susceptibility classes.

Where: LSI = Landslide susceptibility index, FR is the frequency ratio and n is the number of selected causative factors. The calculated values of FR for each pixel in the LSI indicate the relative susceptibility to landslide occurrence. The higher LSI pixel values have high susceptibility to landslide occurrence while the lower LSI pixel values have lower susceptibility (Akgun et al. 2007 ).

Weights of evidence (WoE) model

WoE model is a log-linear form of the Bayesian probability model for landslide susceptibility assessment that uses landslide occurrence as a training point to drive prediction outputs. It calculates both unconditional and conditional probability of landslide hazards. This method is based on the calculation of positive and negative weights to define the degree of spatial association between landslide occurrence and each explanatory variable class (Pardeshi et al. 2013 ). The positive weights (W+) indicate the occurrence of an event while the negative weight (W-) indicates the non-occurrence of an event. To evaluate W + and W - , calculating the following parameters is important.

Nmap = total number of pixels in the map

Nslide = number of pixels with landslides in the class

Nclass = number of pixels in the class

NSLclass = number of pixels with landslides in the class

The values needed for the weight of evidence formula are:

Npix1 = NSLclass

Npix2 = Nslide – NSLclass

Npix3 = Nclass – NSLclass

Npix4 = Nmap – Nslide – Nclass + NSLclass

Then the positive and negative weights are calculated as follows (Equations 3 and 4 ).

Where Npix 1 is the number of landslide pixels present on a given factor class, Npix 2 is the number of landslides pixels not present in a given factor class, Npix 3 is the number of pixels in a given factor class in which no landslide pixels are present and Npix 4 is the number of pixels in which neither landslide nor the given factor is present (Van Westen 2002 ; Dahal et al. 2008 ; Regmi et al. 2010 ). These weights are used to calculate a weight of contrast value (C) for the particular susceptibility variable (Equation 5 ).

The contrast value (C) measures the strength of a relationship between the causative factors and landslides. If the contrast value is positive, it will have a positive spatial association while the negative one will have a negative spatial association. The weighted map ( Wmap ) for each landslide causative factor can be prepared by summing the weights of contrast(C) values of each factor class. Similarly, the final landslide susceptibility index (LSI) map was prepared by summing all the weighted maps (∑ Wmap )  of each landslide causative factor through a raster calculator of map algebra in the spatial analyst tool of ArcGIS as follows (Equations 6 and 7 ).

Landslide inventory

During August, 2018, an intense rainfall in Simada area triggered many landslides that occurred mostly in rural areas. The damage was severe in the villages of Dubdubiya, Asfa Meda, Gedeba, Ditorka and at several other sites along the river courses. Particularly, in Dubdubiya and Asfa Meda villages, landslides damaged 81 dwellings, killed 14 goats, affected thousands of people, damaged hundreds of hectares of farmlands and dislocated 486 people. These problems occurred in these villages as the settlement areas are mostly located at the foot of a steep slope that is covered by weathered volcanic rocks as well as the presence of stream accumulated debris and earth flows that can suddenly burst out at the at the outlets of a mountain. Landslide inventory map of the study area (Fig. 4 ) was prepared from the combination of an intensive field survey and Google Earth image interpretations. Extensive field studies conducted from mid-November to mid-December of 2018 helped us to map known landslides using GPS and check the size and shape of these landslides in order to identify the type of movements, materials involved and to determine the state and activity of landslides (active, reactivated, dormant, etc.). This inventory data was mapped as vector-based polygon data and then converted to the raster format with a pixel size of 30m by 30m in ArcGIS 10.4.

In the present study area, a total of 576 landslides that contain 6304 pixels were identified and divided randomly into training and validation landslides by keeping their spatial distributions into account. The training landslides that accounted 80% of landslides with 5126 pixels were used for building the predictive model while the validation landslides that accounted 20% of landslides with 1178 pixels were used for validating purpose. From the total landslide polygons, 117 landslides were active landslides collected from field investigations while the remaining 459 landslide polygons were collected from time serious Google Earth image interpretations.

Landslide locations are predominantly distributed in the south-central, in the north and in the eastern parts of the study area with decreasing order of landslide density, damage on agricultural land and infrastructures. This area consists of a rugged and mountainous terrain which is characterized by steep slopes, deep gorges, high relief and fractured and weathered rocks. The common types of landslide occurrence in the study area include rock slide, rockfall, earth slide, debris slide and debris flow, rotational and translational soil slide, translational debris slide, rotational debris slide and complex types of slides. Generally, these landslides predominantly affected the rural areas in which the type of landslides and their probable causes and damages are described below.

Most prominent landslides occurred in Asfa Meda, Dubdubiya, Tej Wuha-Gedeba and Ditorka-Megersum Villages. Landslides in Asfa Meda Village occurred at the interface between thin residual soils and rhyolitic rock and most of the landslides are shallow rotational and/or translational earth slides. Most of Dubdubiya village was highly affected by stream undercutting, erosion of the slope surface, riverbank erosion and improper farming practice (Fig. 5 ). The slope materials are dominantly covered by weathered basalt and colluvial deposits. Erosional opening surfaces and tension cracks were observed during field investigation indicating that seeping water might have brought instability of the slope through internal erosion of the weathered materials. A typical example of a landslide in this village was the landslide that occurred near Arata Gabriel Church. The main causes of this landslide were stream/river undercutting, presence of spring on top of the slope and colluvial soil slope materials. The slope material in Teji Wuha and Gedeba Villages is dominantly covered with weathered tuff and thin residual soils. In this village, there is an indication of shallow groundwater since the swamp area and many springs are observed with rotational and soil creep. Creeping of soil was identified by tilting of powerlines and fences (Fig. 6 d). The common types of landslides that were observed in Ditorka and Megersum villages were rockslide (Fig. 6 a), rock fall, debris slide (Fig. 6 b) and rotational slide.

Historical landslide data in Dubdubiya Village as detected from Google Earth image. The white arrow and the red broken line in the figures indicate the direction of the slope movement and a landslide boundary respectively. a Image before the landslide event. b Image after landslide occurrence and, c Photo of the current condition

Field photo of landslide and their damage in different villages of the study area (Red and blue colored arrow indicates the direction of slope movements and River/stream flows respectively). In Ditorka and Megersum Villages- ( a ) Rockslide ( b ) Debris slide; Teji Wuha and Gedeba Villages- ( c ) Earth slide on the gravel road (damaged culvert) ( d ) Creep (tilting of powerline); in Asfa Meda Village- ( e ) and ( f ) Complex type landslides; in Dubdubiya Village- ( g ) Shallow debris flow that damaged wheat crop and ( h ) Rockslide

Landslide causative factors

The spatial distribution and density of landslides are mainly controlled by topography of an area, weather condition, geology, land use/land cover and anthropogenic factors (Khan et al. 2019 ). Consequently, evaluating the impact of these causative factors on the spatial distribution of landslides is very important in order to understand their failure mechanism and to prepare the landslide susceptibility map. In this study, seven causative factors that have been used for the preparation of landslide susceptibility maps include slope, aspect, curvature, lithology, land use/ land cover, rainfall and distance to stream. The roles played by each of these causative factors will be discussed in the following sections.

Slope is a very important parameter for landslide study as it has a direct relation with landslide occurrence. As a result, it is frequently used in preparing a landslide susceptibility map (Yalcin and Bulut 2007 ). It is well known that landslide occurs more frequently on steeper slopes due to gravity stress. The slope map (Fig. 7 a) of the study area was prepared from DEM data. It was divided into five classes such of 0 – 5 0 , 5 0 – 12 0 , 12 0 – 30 0 , 30 0 – 45 0 , and > 45 0 . For slope classes above 12 0 , the frequency ratio is increasing which indicate the higher probability of landslide occurrence in these classes (Table 1 ).

Landslide causative factor maps ( a ) Slope, ( b ) Curvature, ( c ) Distance to stream ( d ) Aspect ( e ) Land use, ( f ) Lithology and ( g ) Rainfall

Curvature map of the study area was generated from DEM data and it was classified into 3 classes of concave, convex and flat surfaces (Fig. 7 b). Following heavy rainfall, a convex or concave slope contains more water and retains this water for a longer period (Lee and Talib 2005 ). The more positive or negative values indicate the higher probability of landslide occurrence. In the flat area, the probability of landslide occurrence is very low. A positive curvature indicates that the surface was upwardly convex at that grid. A negative curvature indicates that the surface was upwardly concave at that grid and a value of zero indicates that the surface is flat.

Aspect refers to the slope orientation which is generally expressed in terms of degree from 0 0 – 360 0 . It is considered as an important factor in landslide studies as it controls slope’s exposure to sunlight, wind direction, rainfall (degree of saturation) and discontinuity conditions (Komac 2006 ). Slope aspect map (Figure 8 c) in this study area was derived from DEM data and it was divided into nine classes, namely; north (0 – 22.5, 337.5 - 360,), northeast, east, southeast, south, southwest, west and northwest (Fig. 7 d).

Landslide Susceptibility Map of the study area using: a FR and b WoE Models

Distance to stream

The proximity of the slope to the stream course is an important factor that dictates the landscape evolution of the area and an indicator of the landslide and related erosional aspects. Rivers with a number of drainage networks have a high probability of landslide occurrence as they erode the slope base and saturate the underwater section of the slope forming material (Akgun and Turk 2011 ).

Since there are many streams in the study area which flow into Kostet, Atkus and Bijena Rivers, many landslides occurred in the close vicinity of these rivers. Hence, this parameter was considered as one causal factor in landslide susceptibility analysis. Zones with parallel pattern of drainage in steep slopes are the most probable landside sites. Drainage often plays its own role in developing pore-water pressure which reduces the shear strength of slope materials. Streamlines were derived from DEM data and it was classified based on stream order.

Landslide in this area is mostly associated with 1 st , 2 nd , and 3 rd order streams. Distance from stream map was developed from Euclidean distance buffering method in the spatial analyst tool of ArcGIS 10.4. This map was classified in to five subclasses: 0 – 50, 50 – 100, 100 – 150, 150 – 200 and > 200 meter (Fig. 7 c).

Land use / land cover

Land-use change has been recognized throughout the world as one of the most important factor influencing the occurrence of rainfall-triggered landslides. Changes in land use/cover resulted from man-made activities such as deforestation, overgrazing, intensive farming and cultivation on steep slope can initiate slope instability (Glade 2003 ). Vegetation has a major contribution to resist slope movements. Vegetation having a well-spread network of root systems increases shearing resistance of the slope material. This is due to the natural anchoring of slope materials. In addition to this, it reduces the action of erosion and adds the stability of the slope. In another way, barren or sparsely vegetated slopes are usually exposed to erosion and thus it has the effect of increasing slope instability. The land use map of the study area was prepared from the Google Earth image of 2016 and the analysis was done in ArcGIS. About seven land-use types were identified including moderate forest, sparse forest, bush, grazing land, agricultural land, settlement and river (Fig. 7 e). The area is predominantly covered by agricultural land and grazing land.

Lithology is one of the most controlling parameters in slope stability since each class of materials has different shear strength and permeability characteristics (Yalcin and Bulut 2007 ). Different rock types have varied composition and structure which contribute to the strength of the slope material in a positive or negative way. The stronger rock units give more resistance to the driving forces as compared to the softer/ weaker rocks. Lithological map of the study area was prepared from existing regional geological map (with a scale of 1:250,000) as a preliminary map for further improvement of a lithologic map into a scale of 1:50,000 based on a detailed field survey. The study area contains seven lithological units namely Trachyte, Weathered tuff, Rhyolite, Weathered basalt, Residual soils, Colluvial and Alluvial Deposits (Fig. 7 f).

Rainfall is considered as an influencing factor to cause slope instability. Precipitation, particularly intense and prolonged rains are controlling factors that trigger landslides by providing water thereby increasing underground hydrostatic level and pore water pressure. When the soil undergoes such pressure changes, water within it will create negative or upward pressure, as it cannot drain quickly. When the pore water pressure is equivalent to the upper pressure, the shearing resistance of the material decrease and will lead to failure of the material. The rainfall data of the four stations that surround the study area were collected from National Metrology Agency of Ethiopia. There are various interpolation techniques in ArcGIS to interpolate rainfall over a large area based on few point data. These include Thiessen polygon, Isohyetal, average arithmetic, inverse distance weight (IDW) and Kriging. The general assumption of the IDW method of interpolation is that the value of unsampled point is the weighted average of known values within the neighborhood. Therefore, the values from a scattered set of known points can be utilized to assign rainfall values to unknown points. It can be used to compute the unknown spatial rainfall data from the known sites that are adjacent to the unknown sites (Chen and Liu 2012 ). The rainfall map of the study area was prepared using the IDW interpolation method in GIS. The rainfall data analysis showed that the maximum monthly rainfall occurs in June, July, August and September which coincides with the landslide occurrence in this area. The rainfall map of the study area was divided into five annual rainfall classes of 627 – 727, 727 – 813, 813 – 901, 901 – 994 and 994 - 1125.2 millimeters (Fig. 7 g) by the natural breaks method.

Result and discussion

Relationship between landslide and causative factors.

This study has analyzed the relationship between seven causative factors and landslide occurrence. Using the FR and WoE models, the relative frequency values and the weights of values were calculated respectively. The causative factors were classified into different classes and weights were assigned to them for both FR and WoE models as presented in Table 1 and 2 respectively. These results showed that the relative susceptibility of each class is almost similar for both models but the parameters and results are different from each other. This implies that if a factor class has lower and higher values in both models, the susceptibility will also be lower and higher respectively. In case of FR model, the spatial relationship between the causative factors and landslide is determined by FR values. The causative factor classes with FR value > 1 will have a high degree of landslide occurrence. On the other hand, for the WoE model, C describes the correlation and spatial association of the landslide with the causative factors. The positive C values indicate a positive association with more landslide occurrence and vice versa for negative C values. The weights with higher values indicate a higher degree of influence on landslide occurrence. Generally, the factor class values derived from each model showed the spatial relationship of the causative factors in their contribution to landslide occurrence. The association is more or less the same in both models.

The slope classes > 12 0 have higher contribution for landslide occurrence. The area with a slope class > 45 0 is the most landslide prone class while the area with a slope class < 5 0 is the least one. Generally, as the slope increases, the probability of landslide occurrence also increases. In case of aspect classes, the FR values of slope classes facing towards the northeast (22.5 – 67.5), east (67.5 – 112.5) and north (0 – 22.5) are greater than one indicating a higher probability of landslide occurrence. The northeast facing aspect class has got the maximum weight or rating followed by the east facing ones. The curvature range of (-3.6) - (-0.001) has a greater contribution to the slope failures. In case of lithology, three units i.e. colluvial deposit, weathered basalt and rhyolite have high probability of landslide occurrence. Colluvial deposit and weathered basalts have less strength and hence susceptible to landslides. Rhyolitic rocks in the study area formed a cliff underlying thin residual soils. As a result, most of the landslides occurred at the contact between rhyolite and thin residual soils.

The type of land use also controls the occurrence of landslide in the study area. The highest weights or ratings were observed in the land use types of grazing land, river, sparse forest and bushes indicating a high probability of landslide occurrence. The highest weighted value of grazing land is due to its exposure to erosion and weathering. In case of the relationship between landslide occurrence and the distance from stream, as the distance from stream increases, the occurrence of landslide generally decreases. Landslide occurrence is higher in the first three classes of 0 – 50m, 50 – 100m and 100 – 150m (Table 1 and 2 ). With regard to the causative factor rainfall, two classes with 813 – 901mm and 901 - 994 mm have a higher C and FR values than the other classes and are the most susceptible classes (Table 1 and 2 ). Generally, slope classes > 20 0 , land use classes of grazing land, sparse forest, river and bush; lithology of colluvial deposit, weathered basalt, alluvial deposit and rhyolite and distance to stream classes of < 150 m buffers are the most contributing factor classes among the seven landslide factor classes.

Landslide susceptibility mapping using FR and WoE models

Frequency ratio model.

Map of each causative factor is prepared with the help of ArcGIS and then the frequency ratio values were calculated. The calculated FR values for each pixel in the LSI indicate the relative susceptibility to landslide occurrence. The higher pixel values of LSI have the higher landslide susceptibility while the lower pixel values will have lower susceptibility (Akgun et al. 2007 ). The landslide susceptibility index was calculated based on the frequency ratio values that have been determined in the training process that can be added in a raster calculator of ArcGIS as follows (Equation 8 ).

Where FR sl = frequency ratio value of slope, Fr as = frequency ratio value of aspect, = FR cu = frequency ratio value of curvature, FR li = frequency ratio value of lithology, FRlu = frequency ratio value of land use, FR rf = frequency ratio value of rainfall, FR ds = frequency ratio value of distance to stream.

The LSI values for the frequency ratio model in the study area range from 2.89 to 15.09. The LSI map is reclassified to prepare the landslide susceptibility map of the study area (Fig. 8 a). There are different types of classification methods such as natural break, equal interval, manual, standard deviation and quantile. In the current study, reliable results were obtained from natural breaks method. The result of other classification methods revealed the susceptibility classes with a high degree of exaggeration where large part of the study area fall into the high susceptibility class.

Therefore, the LSI values were classified into five susceptibility classes of very low (2.89 - 5.31), low (5.31 - 6.24), moderate (6.24 - 7.23), high (7.23 - 8.39) and very high (8.39 - 15.09) using the natural breaks method of classification. The result from Table 3 showed that 8.616% (16 km 2 ), 20.474%(38km 2 ), 29.537%(54.9km 2 ), 27.898% (51.8 km 2 ) and 13.474% (25 km 2 ) areas fall into the very low, low, moderate, high and very high susceptibility classes respectively. As Fig. 8 a clearly shows, the very low and low susceptibility classes are dominantly concentrated in the northwestern and southwestern plateau part of the study area including Welela Bahir, Shomeda, Agona and Jinjero Gedel localities. Similarly, the very high and high susceptibility classes are concentrated in the south central, southeastern and eastern part of the study area particularly in Asfa Meda (Majeta), Dubdubiya (Arata Gebriel) and Ditorka-Megersum respectively and scarcely distributed in the northern part of the study area at Guna-Gedeba Village and in the western part. Moderate susceptibility classes are mostly distributed throughout the study area. The high concentrations of landslides in those high and very high susceptibility classes of the aforementioned areas were due to the presence of colluvial and alluvial deposits, stream undercutting, scattered vegetation cover, man-made activities like intensive farming, deforestation and cultivation.

Weights of evidence model

The landslide susceptibility map of the study area by WoE model was produced based on the weighted values from the seven causative factors and the training landslide (Table 2 ). The difference between W + and W - is known as the weight of contrast which is designated by C = W + - W - . This reflects the overall spatial association between the causative factors and landslides. LSI map of the study area was prepared by summing the weight of contrast values (C) of all the seven causative factors using a raster calculator in ArcGIS as follows:

Where LSI = landslide susceptibility index; C sl = weight contrast value of slope, C as = weight contrast value of aspect, C cu = weight contrast value of curvature, C li = weight contrast value of lithology, C lu = weight contrast value of land use, C rf = weight contrast value of rainfall, C ds = weight contrast value of distance to stream.

The LSI values for the WoE model in the study area range from -7.84 to 4.52. The LSI map is reclassified by the natural breaks method of classification technique in order to prepare the landslide susceptibility map of the study area (Fig. 8 a). Then, the LSI values were classified in to five susceptibility zones of very low (-7.84 - -3.72), low (-3.72 - -1.83), moderate (-1.83 - -0.28), high (-0.28 – 1.17) and very high (1.17 – 4.52). The result from Table 4 showed that 8.448%(15.7 km 2 ), 21.408%(39.8km 2 ), 33.140%(61.5km 2 ), 23.787% (44.2 km 2 ) and 13.216% (24.5 km 2 ) area fall in the very low, low, moderate, high and very high susceptibility classes respectively.

Validation of the model

Without model validation, landslide susceptibility maps will not be meaningful. As a result, validation of the predictive model is an important step for landslide susceptibility mapping (Bui et al. 2012 ). A predictive model map was constructed by overlying 80% of the landslides (training) over the causative factors. This model was validated using validation landslides (20%) that were not used for building the model. There are various types of validation techniques for landslide susceptibility maps. In the current study, the performance of the LSMs produced by FR and WoE models were evaluated using Area Under the Curve (AUC) and Landslide Density Index (LDI).

Area under the curve (AUC)

The area-under-curve (AUC) method works by creating success rate and prediction rate curves (Lee 2005 ). Landslide susceptibility maps can be validated by comparing the susceptibility maps with both the training landslide (80%) and validation landslide (20%). The success and predictive rate curves can be created for both FR and WoE models. The success rate curve is based on the comparison between the predictive model and the training landslide. The predictive rate curve is based on the comparison between the predicted map and the validation landslide. The Area Under the Curve (AUC) of the success rate represents the quality of the model to reliably classify the occurrence of existing landslides whereas the AUC of the predictive rate explains the capacity of the proposed landslide model for predicting landslide susceptibility (Pamela et al. 2018 ). AUC was calculated by reclassifying LSI into 50 classes with descending order of the values of pixels in the study area and combined with a landslide inventory. Then the rate curves were drawn through the cumulative percentage of both the training and validation landslide (y-axis) and cumulative area percentage (x-axis). The result showed that both models exhibited very good performances. However, the FR model is better with a success rate of 89.8% and a predictive rate of 88.2% than the WoE model with a success rate of 86.5% and a predictive rate of 84.8% (Fig.  9 ).

Success and Predictive rate Curves of FR and WoE models

Landslide density index (LDI)

For validation of the model, landslide pixels which have not been used for constructing the models are generally considered as the future landslide area. In this work to check the validation of the landslide susceptibility model, the testing samples that consist of 20% of the landslide pixels were overlaid over the landslide susceptibility map. The landslide density index, which is the ratio between the percentage of landslide pixels and the percentage of class pixels in each class on landslide susceptibility map, was used to validate the model (Pham et al. 2015 ). If the value of the landslide density index is increased from low to a very high susceptibility classes, then the landslide susceptibility map is considered to be valid. LDI can be calculated using the formulae in eq. 10 below and its output was presented in Table  5 . The suitability of any susceptibility map can be validated if more percentages of landslides occur in the high and very high susceptibility zones as compared to other zones (Fayez et al. 2018 ).

From Table 5 , it can be observed that the landslide density values for very high susceptibility classes are 2.743 and 2.993 with respect to WoE and FR models which are remarkably higher than the other classes. In addition to this, there is a gradual decrement in landslide density values from very high to very low susceptibility classes (Fig.  10 ). This indicates the validity of the landslide susceptibility map. Can et al. ( 2005 ) and Bai et al. ( 2010 ) stated that the landslide data should lie in either the high or very high susceptibility classes for successful validation of a LSM. Table 5 , Figs.  10 and 11 illustrate the characteristics of susceptibility classes for validation of both FR and WoE models. In general, the presence of the highest landslide percentage and density in the very high landslide susceptibility class indicates the reliability of the landslide susceptibility maps produced from FR and WoE models.

Landslide density of FR and WoE models for both training and validation landslide

Bar diagram showing the landslide percentages in different landslide susceptibility classes

Landslide posed a significant impact at Simada District of South Gondar Zone in northwestern Ethiopia on human and animal lives, agricultural lands, settlements, infrastructures and also affected the social and economic aspects of the rural community. To investigate this problem, landslide susceptibility mapping has been carried out using FR and WoE models for proper land use planning, development and management of landslide prone areas. For this, a landslide inventory map of the study area with a total of 576 landslides was divided into training and validation landslides with 80 % and 20% respectively. Seven landslide causative factors including slope, aspect, curvature, lithology, land use, rainfall and distance to stream were considered to analyze, evaluate and establish the spatial relation of these factors with landslides. From FR values and WoE contrast values, it was possible to identify which factor classes are playing a significant role for the occurrence of landslides in the study area. The FR values that are greater than 1 and the WoE contrast (C) values that are greater than 0 were found in the factor classes of slope greater than 12°; curvature classes (-3.60) - (-0.001); aspect classes facing towards N (0 – 22.5), NE (22.5 – 67.5) and E (67.5 – 112.5); distance to stream classes (< 150m); land use classes (grazing land, river, sparse forest and bushes); lithology classes (colluvial deposit, alluvial deposit, weathered basalt and rhyolite), rainfall classes (813 – 901mm and 901 – 994 mm). The LSI map of the study area was prepared based on FR values and WoE contrast values in ArcGIS 10.4 using the spatial analyst tools of raster calculator for both FR and WoE models. The LSI map in each model was reclassified into five landslide susceptibility classes of low, low, moderate, high and very high based on the natural breaks method of classification to produce the final landslide susceptibility maps. The performance of the final landslide susceptibility maps produced by FR and WoE models were validated using Landslide Density Index (LDI) and Area Under the Curve (AUC) values. The result revealed that the very low, low, moderate, high and very high values of the landslide susceptibility map are comparable with Landslide Density Index. In case of AUC, the rate curves were drawn using the cumulative percentage of the landslide in the Y-axis and cumulative percentage of map area in the X-axis. The results showed that both models exhibited very good performance. However, the FR model, which showed a success rate of 89.8% and a prediction rate of 88.2%, is better than the WoE model with a success rate of 86.5% and a prediction rate of 84.8%. This study confirmed that the bivariate statistical methods of FR and WoE models were found to be simple and effective models for landslide susceptibility mapping in the Guna mountainous chain of Simada area. The landslide susceptibility maps of the study area were prepared with a scale of 1:50,000 which can be used by civil engineers, geologists, designers and decision-makers for regional land use planning, site selection and landslide prevention and mitigation purposes.

Recommendation

The present study showed the importance of integrating various factors that are responsible for landslide occurrence in the study area. However, the quality of landslide inventory and the causative factor maps should be improved with good quality in time and space. Landslide in the study area has affected the local people who are living near to mountainous area, valleys and gorges. Their animals were died, houses and agricultural lands were destroyed and both social and economic activities were affected. Hence, besides preparing the landslide susceptibility maps of the area, suggesting the necessary preventive measures in the high and very high susceptibility classes is very essential in order to reduce the impact of future landslide hazards in the area. Hence, this study recommends planting trees & vegetation, providing proper drainage, applying gabion and check dam, relocating people and creating public awareness. In order to implement these remedial measures, further study on the geotechnical properties of soils and rocks should be conducted in this area.

Availability of data and materials

Rainfall data was collected from National Metrology Agency of Ethiopia. Topographic Map was purchased from Ethiopian Geospatial Information Agency. DEM data was freely available from http://gdex.cr.usgs.gov/gdex/ website.

Abay A, Barbieri G (2012) Landslide susceptibility and causative factors evaluation of the landslide area of Debresina, in the southwestern Afar escarpment, Ethiopia. J Earth Sci Eng 2:133–144

Google Scholar  

Abebe B, Dramis F, Fubelli G, Umar M, Asrat A (2010) Landslides in the Ethiopian highlands and the rift margins. African Earth science 56:131–138

Article   Google Scholar  

Akgun A, Dag S, Bulut F (2007) Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Eng Geol V 54:1127–1143

Akgun A, Turk N (2011) Mapping erosion susceptibility by multivariate statistical method: a case study from the Ayvalik region. NW Turkey Computers & Geosciences, Turkey

Akgun AA (2012) Comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey. Landslides 9:93–106

Aleotti P, Chowdhury R (1999) Landslide Hazard assessment: summary, review and new perspectives. Bull Eng Geol Environ 58:21–44

Ayalew L (1999) The effect of seasonal rainfall on landslides in the highlands of Ethiopia. Bull Eng Geol Environ 58:9–19

Ayalew L, Yamagishi H (2005) The application of GIS based logistic regression for landslide susceptibility mapping in the KakudaYahiko Mountains Central Japan. Geomorphology 65(1):15–31

Baharin A, Farshid S, Javad M, Barat M (2014) Using Frequency Ratio Method for Spatial Landslide Prediction. Res J App Sci Eng Technol. 7:3174–3180

Bai SB, Wang J, Lu G-N, Zhou P-G, Hou S-S, Xu S-N (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the three gorges area, China. Geomorphology 115:23–31 https://doi.org/10.1016/j.geomorph.2009.09.025

Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

Can, Nefeslioglu HA, Gokceoglu C, Sonmez H, Duman TY (2005) Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyses

Book   Google Scholar  

Chen FW, Liu CW (2012) Estimation of the spatial rainfall distribution using inverse distance weighting (IDW) in the middle of Taiwan. Paddy Water Environ 10:209–222

Choi J, Oh H-J, Lee H-J, Lee C, Lee S (2012) Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol 124:12–23

Crozier MJ (1986) Landslides: causes, consequences & environment. Croom Helm Pub, London

Cruden DM (1991) A simple definition of a landslide. Bulletin of the International Association of Engineering Geology 43(1):27–29. https://doi.org/10.1007/bf02590167

Cruden DM, Varnes DJ (1996) Landslide types and processes. National Research Council, Washington DC

Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Dhakal S, Paudyal P (2008) Predictive modelling of rainfall-induced landslide hazard in the Lesser Himalaya of Nepal based on weights-of-evidence. Geomorphology 102:496–510

Dai FC, Lee CF, Ngai YY (2002) Landslide risk assessment and management: an overview. Eng Geol 64:65–87

Demir G, Aytekin M, Akgün A, Ikizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65:1481–1506

Dikau R, Brunsden D, Schrott L, Ibsen M-L (eds) (1996) Landslide recognition. Identification, movements and causes. Wiley, Chichester, p 251

Fayez L, Pham BT, Solanki HA, Pazhman D, Dholakia MB, Khalid M, Prakash I (2018) Application of frequency ratio model for the development of landslide susceptibility mapping at part of Uttarakhand state, India. Int J Appl Eng Res 13:6846–6854

Felicisimo ÁM, Cuartero A, Remondo J, Quirós E (2012) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10:175–189

Gholami M, Ghachkanlu EN, Khosravi K, Pirasteh S (2019) 2018 Landslide prediction capability by comparison of frequency ratio, fuzzy gamma and landslide index methods. J Earth Syst Sci 128:42 https://doi.org/10.1007/s12040-018-1047-8 Indian Academy of Sciences

Girma F, Raghuvanshi TK, Ayenew T, Hailemariam T (2015) Landslide hazard zonation in Ada Berga district, Central Ethiopia – a GIS-based statistical approach. J Geom 90:25–38

Glade T (2003) Landslide occurrence as a response to land-use change: a review of evidence from New Zealand. Catena 51(3–4):297–314

Glade T, Anderson M, Crozier MJ (eds) (2005) Landslide Hazard and Risk. https://doi.org/10.1002/9780470012659

Guzzetti F, Mondini AC, Cardinali M, Fiorucci F, Santangelo M, ChangM KT (2012) Landslide inventory maps: new tools for an old problem. Earth Sci Rev 112:42–66 Elsevier

Hong H, Xu C, Bui DT (2015) Landslide susceptibility assessment at the Xiushui area (China) using frequency ratio model. Procedia Earth and Planetary Science 15:513–517

Hutchinson JN (1989) General report: morphological and geotechnical parameters of landslides in relation to geology and hydrogeology. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts 26(2):88. https://doi.org/10.1016/0148-9062(89)90310-0

Kanungo D, Arora M, Sarkar S, Gupta R (2009) Landslide susceptibility zonation (LSZ) mapping-a review. J South Asia Disaster Stud 2:81–105

Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85:347–366

Khan H, Shafique M, Khan M, Bacha M, Shah S, Calligaris C (2019) Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. Egyptian J Remote Sensing Space Sci 22(1):11–24. https://doi.org/10.1016/j.ejrs.2018.03.004

Komac M (2006) A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74(1–4):17–28

Lee S (2005) Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data. Int J Remote Sens 26(7):1477–1491. https://doi.org/10.1080/01431160412331331012

Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4:33–41

Lee S, Talib JA (2005) Probabilistic landslide susceptibility and factor effect analysis. Environ Geol 47:982–990

Article   CAS   Google Scholar  

Meten M, Bhandary NP, Yatabe R (2015a) Effect of landslide factor combinations on the prediction accuracy of landslide susceptibility maps in the Blue Nile gorge of Central Ethiopia. Geoenviron Disast 2:9 https://doi.org/10.1186/s40677-015-0016-7

Meten M, Bhandary NP, Yatabe R (2015b) GIS-based frequency ratio and logistic regression modeling for landslide susceptibility mapping of Debre Sina area in Central Ethiopia. J Mt Sci 12(6). https://doi.org/10.1007/s11629-015-3464-3

Mezughi TH, Akhir JM, Rafek AG, Abdullah I (2011) Landslide susceptibility assessment using frequency ratio model applied to an area along the E-W highway (Gerik-Jeli). Am J Environ Sci 7:43–50

Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster-Shafer, and weights-of-evidence models. J Asian Earth Sci. 61:221–236

Mondal S, Maiti R (2013) Integrating the analytical hierarchy process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of shiv-khola watershed, Darjeeling Himalaya. Int J Disaster Risk Sci 4:200–212

Pamela, Sadisun IA, Arifianti Y (2018) Weights of evidence method for landslide susceptibility mapping in Takengon, Central Aceh, Indonesia. IOP Conference Series: Earth and Environmental Science, 118:012037. https://doi.org/10.1088/1755-1315/118/1/012037

Pan X, Nakamura H, Nozaki T, Huang X (2008) A GIS-based landslide hazard assessment by multivariate analysis landslides. J Jpn Landslide Soc 45(3):187–195

Pardeshi SD, Autade SE, Pardeshi SS (2013) Landslide hazard assessment: recent trends and techniques. SpringerPlus 2(1). https://doi.org/10.1186/2193-1801-2-523

Parise M, Jibson RW (2000) A seismic landslide susceptibility rating of geologic units based on analysis of characteristics of landslides triggered by the 17 January, 1994 Northridge, California earthquake. Eng Geol 58:251–270

Park S, Choi C, Kim B, Kim J (2012) Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea. Environ Earth Sci 68:1443–1464

Paulin L, Bursik G, Hubp M, Mejía JL, Aceves Quesada FLMP (2014) A GIS method for landslide inventory and susceptibility mapping in the Río El Estado watershed, Pico de Orizaba volcano, México. Nat Hazards 71:229–241

Peng L, Niu R, Huang B, Wu X, Zhao Y, Ye R (2014) Landslide susceptibility mapping based on rough set theory and support vector machines: a case of the three gorges area, China. Geomorphology 204:287–301 Elsevier B.V

Pham BT, Tien Bui D, Prakash I, Dholakia M (2015) Landslide susceptibility assessment at a part of Uttarakhand Himalaya, India using GIS–based statistical approach frequency ratio method. Int J Eng Res Technol 4:338–344

Pirasteh S, Li J (2017) Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs. Pirasteh Li Geoenviron Dis 4:19 https://doi.org/10.1186/s40677-017-0083-z

Pradhan B, Lee S, Buchroithner MF (2010) Remote Sensing and GIS-based Landslide Susceptibility Analysis and its Cross-validation in Three Test Areas Using a Frequency Ratio Model. Photogrammetrie-FernerkundungGeoinformation 2010(1):17–32. https://doi.org/10.1127/1432-8364/2010/0037

Rahmati O, Haghizadeh A, Pourghasemi HR, Noormohamadi F (2016) Gully erosion susceptibility mapping: the role of GIS-based bivariate statistical models and their comparison. Nat Hazards 82(2):1231–1258 https://doi.org/10.1007/s11069-016-2239-7

Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2013) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7(2):725–742 https://doi.org/10.1007/s12517-012-0807-z

Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115(1-2):172–187. https://doi.org/10.1016/j.geomorph.2009.10.002

Temesgen B, Mohammed U, Asfawosen A (1999) Landslide hazard on the Dabicho, Wondo genet area: the case of June 18, 1996 event. SENET: Ethiop J Sci 22(l):l27–l140

Temesgen B, Mohammed U, Korme T (2001) Natural Hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Phys Chem Earth Part C: Solar Terrestr Planet Sci (C) 26(9):665–615

Vakhshoori V, Zare M (2016) Landslide susceptibility mapping by comparing the weight of evidence, fuzzy logic, and frequency ratio methods. Geomatics Nat Hazards Risk. 7(5):1731–1752

Van Westen CJ (2002) Use of weights of evidence modeling for landslide susceptibility mapping. International Institute for Geoinformation Science and Earth Observation (ITC), Enscheda

Varnes DJ (1978) Slope movements: types and processes. In: Schuster, R.L. and Krizek, R.J. (eds.) Landslide analysis and control, National Academy of Sciences, Transportation Research Board Special Report 176, Washington, 11-33.

Wang Q, Li W (2017) A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr 38(4):318–337

Woldearegay K (2008) Characteristics of a large-scale landslide triggered by heavy rainfall in Tarmaber area, central highlands of Ethiopia. Geophys Res Abstr 10:EGU2008-A-04506 2008, SRef – ID:16077962/gra/EGU2008-A-04506

Woldearegay K (2013) Review of the occurrences and influencing factors of landslides in the highlands of Ethiopia. Momona Ethiop J Sci (MEJS) 5(1):3–31

WP/WLI - International Geotechnical Societies’ UNESCO Working Party on World Landslide Inventory (1990) A suggested method for reporting a landslide. Int Assoc Eng Geol Bull 41:5–12

Yalcin A, Bulut F (2007) Landslide susceptibility mapping using GIS and digital photogrammetric techniques: a case study from Ardesen (NE-Turkey). Nat Hazards 41:201–226

Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85(3):274–287 https://doi.org/10.1016/j.catena.2011.01.014

Ye C, Li Y, Cui P, Liang L, Pirasteh S, Marcato J et al (2019) Landslide Detection of Hyperspectral Remote Sensing Data Based on Deep Learning With Constrains. IEEE J Selected Topics Appl Earth Observ Remote Sensing 12(12):5047–5060. https://doi.org/10.1109/jstars.2019.2951725

Yilmaz I (2009) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat—Turkey). Comput Geosci 35(6):1125–1138

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Acknowledgments

The first author would like to thank Dr. Kifle Woldearegay, Mr. Azmeraw Wubalem and Mr. Leulalem Shano for thier useful Comments and suggestions to improve this research work. He would also like to thank Mizan Tepi University for providing the scholarship opportunity to pursue his MSc study at Addis Ababa Science and Technology University.

Mizan Tepi University partially funded the first author’s MSc thesis research work.

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Tilahun Mersha & Matebie Meten

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TM as a first author has mostly participated in the whole process of this research work including the field-work, data collection, database preparation and in compiling the results by himself. MM, as an advisor, commented in each phase of the research for further improvement. TM addressed the comments given from his advisor in terms of its scientific justification, methodological aspect and English correction before the final submission and finally the two authors approved this paper submission.

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Mersha, T., Meten, M. GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenviron Disasters 7 , 20 (2020). https://doi.org/10.1186/s40677-020-00155-x

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DOI : https://doi.org/10.1186/s40677-020-00155-x

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A CASE STUDY ON A LANDSLIDE: A GEOTECHNICAL INVESTIGATION

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Landslide Disasters: Seeking Causes – A Case Study from Uttarakhand, India

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• J. S. Rawat 6  

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This study is about the fundamental causes and character of landslides in the Himalaya and similar mountain belts. In part, it is intended as a protest against the repetitive and misleading reports that so often follow each successive extreme rainfall event and consequent landslide swarm. The problem is that many of these.‘kneejerk’. reactions to a disaster do little more than support folklore or a particular political stance.

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The rwenzori mountains, a landslide-prone region.

Introduction: Landslide Disasters and Relief

Landslide Impacts in Europe: Weaknesses and Strengths of Databases Available at European and National Scale

Ayalew, L. and Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology , 65: 15-31.

Article   Google Scholar  

Barbieri, G. and Cambuli, P. (2009). The weight of evidence: statistical method in landslide susceptibility mapping of the Rio Pardu Valley (Sardinia, Italy). 18th World IMACS/MODSIM Congress, Cairns, Australia 13-17 July 2009. Proceedings, pp 2659-2664. Available at: http://www.mssanz.org.au/modsim09/G3/barbieri.pdf (August 20, 2010).

Barnard, P.L., Owen, L.A., Sharma, M.C. and Finkel, R.C. (2001). Natural and human- induced landsliding in the Garhwal Himalaya of northern India. Geomorphology , 40: 21-35.

Brown, C. and Leibovitch, L. (2010). Fractal Analysis. Sage, Thousand Oaks, CA.

Google Scholar  

Bruijnzeel, L.A. and Bremmer, C.N. (1989). Highland-lowland interactions in the Ganges Brahmaputra river basin: A review of published literature. Kathmandu, Nepal, International Centre for Integrated Mountain Development (ICIMOD), Occasional Paper.

Brunetti, M.T., Guzzetti, F. and Rossi, M. (2009). Probability distributions of landslide volumes. Nonlinear Processes in Geophysics , 16: 179-188.

Carrara, A., Catalano, E., Reali, C. and Sorriso-Valvo, S. (1982). Computer assisted technologies for regional landslide evaluation. Studia Geomorphologica Carpatho- Balcanica , 15: 99-113.

Champati Ray, P.K., Dimri, S., Lakhera, R.C. and Sati, S. (2007). Fuzzy-based method for landslide hazard assessment in active seismic zone of Himalaya. Landslides , 4(2): 101-111.

Corominas, J., Copons, R., Vilaplana, J.M., Altimir, J. and Amigó, J. (2003). Integrated Landslide Susceptibility Analysis and hazard assessment in the Principality of Andorra. Natural Hazards , 30(3): 421-435.

Cruden, D.M. and Varnes, D.J. (1996). Landslide types and processes. In: Turner, A.K. and Schuster, R.L. (eds). Landslides: Investigation and Mitigation. National Academy Press, Washington, DC. Special Report, 36-75.

Dahal, R.K. and Hasegawa, S. (2008). Representative rainfall thresholds for landslides in the Nepal Himalaya. Geomorphology , 100: 429-443.

Dapples, F., Lotter, A.F., van Leeuwen, J.F.N., van der Knaap, W.O., Dimitriadis, S. and Oswald, D. (2002). Paleolimnological evidence for increased landslide activity due to forest clearing and land-use since 3600 cal BP in the western Swiss Alps. Journal of Paleolimnology , 27(2): 239-248.

District Administration, Almora (2011). Natural calamity. District Administration, Almora, Uttarakhand. Available at: http://almora.nic.in/pages/display/66-casualty-damage (accessed October, 2011).

Froehlich, W., Gil, E., Kasza, I. and Starkel, L. (1989). Thresholds in the transformation of slopes and river channels in the DarjeelingHimalaya, India. Studia Geomor- phologica Carpatho-Balcanica , 23: 105-121.

Gerrard, J. and Gardner, R. (2002). Relationships between landsliding and land use in the Likhu Khola Drainage Basin, Middle Hills, Nepal. Mountain Research and Development , 22(1): 48-55.

Glade, T. (2003). Landslide occurrence as a response to land use change: A review of evidence from New Zealand. Catena , 51: 297-314.

Glade, T., Crozier, M. and Smith, P. (2000). Applying Probability Determination to Refine Landslide-triggering Rainfall Thresholds Using an Empirical "Antecedent Daily Rainfall Model". Pure and Applied Geophysics , 157: 1059-1079.

Gray, D.H. and Leiser, A.T. (1982). Biotechnical Slope Protection and Erosion Control. Van Nostrand-Reinhold, New York.

Guzzetti, F., Carrara, A., Cardinali, M. and Reichenbach, P. (1999). Landslide hazard evaluation: A review of current techniques and their applications in a multi-scale study, Central Italy. Geomorphology , 31: 181-216.

Guzzetti, F., Peruccacci, S., Rossi, M. and Stark, C.P. (2007a). The rainfall intensity- duration control of shallow landslides and debris flows: An update. Landslides , 5(1): 3-17.

Guzzetti, F., Peruccacci, S., Rossi, M. and Stark, C.P. (2007b). Rainfall thresholds for the initiation of landslides. Meteorology and Atmospheric Physics , 98: 239-267.

Haigh, M.J. (2002). Landslide prediction: Criticality, magnitude and frequency. In: Subramanian, V. (ed.). Environmental Hazards in South Asia. Capital Publishing Company, New Delhi.

Haigh, M.J. (1993). Landslide ecology: Mussoorie-Tehri road. In: Rajwar, G.S. (ed.) Garhwal Himalaya: Ecology & Environment. Ashish, New Delhi.

Haigh, M.J. (1984). Landslide prediction and highway maintenance in the Lesser Himalaya, India. Zeitschrift fur Geomorphologie , Suppl. Bd 51, 17-38.

Haigh, M.J., Rawat, J.S. and Bartarya, S.K. (1988). Environmental correlations of landslide frequency along new highways in the Himalaya: Preliminary results. Catena , 15(6): 539-553.

Haigh, M.J., Rawat, J.S. and Bartarya, S.K. (1987). Impact of hill roads on downslope forest cover. Himalaya: Man and Nature , 11(4): 2-3.

Haigh, M.J., Rawat, J.S., Bartarya, S.K. and Rawat, M.S. (1993). Environmental influences on landslide activity, Almora Bypass, Kumaun Lesser Himalaya. Natural Hazards , 8(2): 153-170.

Haigh, M.J., Rawat, J.S., Bartarya, S.K. and Rawat, M.S. (1992). Analysis of landslide sites: Kilbury Road, Kumaun Himalaya. Current Science , 62(7): 518-522

Haigh, M.J., Rawat, J.S., Rawat, M.S., Bartarya, S.K. and Rai, S.P. (1995). Interactions between forest and landslide activity along new highways in the Kumaun Himalaya. Forest Ecology & Management , 78: 173-189.

Howell, J. (1999). Roadside Bio-engineering Reference Manual. HM Government of Nepal, Department of Roads, Kathmandu.

Huang, Z., Law, K.T., Liu, H. and Jiang, T. (2009). The chaotic characteristics of landslide evolution: A case study of Xintan landslide. Environmental Geology , 56(8): 1585-1591.

Joshi, V. and Kumar, K. (2006). Extreme rainfall events and associated natural hazards in Alaknanda valley, Indian Himalayan region. Journal of Mountain Science , 3(3): 228-236.

Kumar, K., Rawat, D.S. and Joshi, R. (1997). Chemistry of springwater in Almora, Central Himalaya, India. Environmental Geology , 31(3-4): 150-156.

Kapur, A. (2010). Vulnerable India: A Geographical Study of Disasters. Sage, New Delhi.

Klecka, W.R. (1975). Discriminant analysis. In: Nie, N.H., Hull, C.H., Jenkins, J.G., Steinbrenner, K. and Bent, D.H. (eds). SPSS: Statistical Package for the Social Sciences (2e). McGraw Hill, New York.

Korup, O. and Weidinger, J.T. (2011). Rock type, precipitation, and the steepness of Himalayan threshold hillslopes. Geological Society, London, Special Publications , 353: 235-249.

Laszlo, E. (1972). The Systems View of the World. G. Braziller, New York.

Larsen, M.C. and Parks, J.E. (1997). How wide is a road? The association of roads and mass-wasting in a forested montane environment. Earth Surface Processes and Landforms , 22(9): 835-848.

Lee, S. and Min, K. (2001). Statistical analysis of landslide susceptibility at Yongin, Korea. Environmental Geology , 40(9): 1095-1113.

Lockwood, D.R. and Lockwood, J.A. (2008). Grasshopper population ecology: Catastrophe, criticality, and critique. Ecology and Society , 13(1): 34. Available at: http://www.ecologyandsociety.org/vol13/iss1/art34/ (accessed November 2011).

Lockwood, D.R. and Lockwood, J.A. (1997). Evidence of self-organized criticality in insect populations. Complexity , 2: 49-58.

Maharaja, R.J. (1993). Landslide processes and landslide susceptibility analysis from an upland watershed: A case study from St. Andrew, Jamaica, West Indies. Engineering Geology , 34(1-2): 53-79.

Magliulo, P., Di Lisio, A. and Filippo Rus, F. (2009). Comparison of GIS-based methodologies for the landslide susceptibility assessment. Geoinformatica , 13(3): 253-265.

Mandelbrot, B. (1967). How long is the coast of Britain? Statistical Self-Similarity and Fractional Dimension. Science , NS, 156(3775): 636-638. Available at: http:/ /users.math.yale.edu/~bbm3/web_pdfs/howLongIsTheCoastOfBritain.pdf (accessed November 2011).

Montgomery, D.R., Schmidt, K.M., Greenberg, H.M. and Dietrich, W.E. (2000). Forest clearing and regional landsliding. Geology , 28(4): 311-314.

Noever, D.A. (1993). Himalayan sandpiles. Physical Review , E47(1): 724-725.

Pande, A., Joshi, R.C. and Jalal, D.S. (2002). Selected landslide types in the Central Himalaya: their relation to geological structure and anthropogenic activities. The Environmentalist , 22(3): 269-287.

Pande, R.K. (2006). Landslide problems in Uttaranchal, India: Issues and challenges, Disaster Prevention and Management , 15(2): 247-255.

Panikkar, S.V. and Subramanyan, V. (1996). A geomorphic evaluation of the landslides around Dehradun and Mussoorie, Uttar Pradesh, India. Geomorphology , 15(2): 169-181.

Pant, P.D. (1990). Geological and Geomophological Investigations of Laharkhet- Karmi area, with special emphasis landslide study, District Almora, Uttar Pradesh. Kumaun University, Nainital, Uttarakhand. Faculty of Science, PhD thesis.

Parry, S. (2011). The application of geomorphological mapping in the assessment of landslide hazard in Hong Kong. Developments in Earth Surface Processes , 15: 413-442.

Parthasarathy, A. (2001). Choice Upanishads. Parthasarathy, Mumbai.

Pelletier, J.D., Malamud, B.D., Blodgett, T. and Turcotte, D.L. (1997). Scale-invariance of soil moisture variability and its implications for the frequency-size distribution of landslides. Engineering Geology , 48(3-4): 255-268.

Rautelaa, P. and Paulb, S.K. (2001). August 1998 landslide tragedies of central Himalayas (India): learning from experience. International Journal of Environmental Studies , 58(3): 343-355.

Rautelaa, P. and Lakhera, R.C. (2000). Landslide risk analysis between Giri and Tons Rivers in Himachal Himalaya (India). International Journal of Applied Earth Observation & Geoinformation , 2(3-4): 153-160.

Rawat, J.S. (2010). GIS of Almora Disaster 2010. Kumaun University Centre for Natural Resources Data Management System, Almora.

Rawat, J.S. (2000). Land Creeping and Slumping due to Accelerated Infiltration and Dip-slope Cutting across an Aquifer in the Kumaun University Campus Almora Hillslope, Technical Report. Kumaun University Department of Geography, Almora, Uttarakhand.

SAARC Secretariat (1992). Chapter 13: Mass Wasting and Land Slides. In: Regional Study on the Causes and Consequences of Natural Disasters and the Protection and Preservation of the Environment. South Asian Association for Regional Cooperation, Kathmandu. Available at: http://www.saarc-sec.org/userfiles/Large%20Publications/CCNDPPE/17-CCNDPPE-Chapter%20XIII%20-%20Mass%20Wasting%20and%20Land%20Slides.pdf (accessed September, 2011).

Sah, N.K. and Pande, R.K. (1987). Construction activity and environmental degradation in Almora Town in the Central Himalaya. Mountain Research and Development , 7(1): 71-75.

Sarkar, S. and Kanungo, D.P. (2002). Landslides in relation to terrain parameters - A Remote Sensing and GIS approach. GIS development.net, Application: Natural Hazard Management: Landslides & Soil Erosion , 14: 5. Available from: http://www.gisdevelopment.net/application/natural_hazards/landslides/nhls0010pf.htm . (accessed January 5, 2011).

Sati, S.P., Sundriyal, Y.P., Naresh, R. and Surekha, D. (2011). Recent landslides in Uttarakhand: Nature's fury or human folly. Current Science , 100(11): 1617-1620.

Sati, S.P., Naithani, A. and Rawat, G.S. (1998). Landslides in the Garhwal Lesser Himalaya, UP, India. The Environmentalist , 18(3): 149-155.

Suzuki, T. (2002). Rock control in geomorphological processes: Research history in Japan and perspective. Japanese Geomorphological Union, Transactions , 23(2): 161-199.

Stark, C.P. and Hovius, N. (2001). The characterization of landslide size distributions. Geophysical Research Letters , 28(6): 1091-1094.

Tamura, T. (2008). Occurrence of hillslope processes affecting riparian vegetation in upstream watersheds of Japan. In: Hitoshi Sakio, H. and Tamura, T. (eds). Ecology of Riparian Forests in Japan: disturbance, life history and regeneration. Springer, Tokyo.

Tolia, R.S., Sharma, R., Pande, R.K. and Pathak, J.K. (2004). Landslide problems in the Uttarakhand region. In: Sahni, P., Dhameja, A. and Medury, U. (eds). Disaster Mitigation: Experiences and Reflections. Prentice Hall of India, New Delhi.

Valdiya, K.S. (1980). Geology of the Kumaun Lesser Himalaya. Wadia Institute for Himalayan Geology, Dehra Dun, Uttarakhand, 220 pp.

Valdiya, K.S. (1988). Geology of Kumaun: An outline. In: Valdiya, K.S. (ed.). Kumaun Land and People. Gyanodaya Prakashan, Nainital, Uttarakhand.

van Westen, C.J., van Asch, T.W.J. and Soeters, R. (2006). Landslide hazard and risk zonation - Why is it still so difficult? Bulletin of Engineering Geology and the Environment , 65(2): 167-184.

Whitehead, A.N. (1920). The Concept of Nature. Cambridge University Press, Cambridge.

Wieczoreck, G.F. (1996). Landslide triggering mechanisms. In: A.K. Turner and Schuster, R.L. (eds). Landslides: Investigation and Mitigation. Transportation Research Board, National Academy of Sciences, Washington, D.C., Special Report 247, pp. 76-90.

Wu, S., Wang, H., Han, J., Shi, J., Shi, L. and Zhang, Y. (2009). The application of Fractal Dimensions of landslide boundary trace for evaluation of slope instability landslide disaster mitigation in Three Gorges Reservoir, China. Environmental Science and Engineering , 3: 465-474.

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Haigh, M., Rawat, J.S. (2012). Landslide Disasters: Seeking Causes – A Case Study from Uttarakhand, India. In: Krecek, J., Haigh, M.J., Hofer, T., Kubin, E. (eds) Management of Mountain Watersheds. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2476-1_18

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By Lewis Jackson and Renju Jose

SYDNEY (Reuters) – The Papua New Guinea government ordered thousands of residents to evacuate from the path of a still-active landslide on Tuesday, after parts of a mountain collapsed, burying at least 2,000 people according to the government’s estimates.

Officials said the odds of finding survivors were slim, even as relief teams have trickled into the difficult-to-access northern Enga region of the Pacific nation since Friday.

Heavy equipment and aid have been slow to arrive because of the treacherous terrain and tribal unrest in the remote area, forcing the military to escort convoys of relief teams.

Residents have been using shovels and their bare hands to search for survivors.

“The landslide area is very unstable. When we’re up there, we’re regularly hearing big explosions where the mountain is, there are still rocks and debris coming down,” Enga province disaster committee chairperson Sandis Tsaka told Reuters.

Military personnel had set up checkpoints and were helping move residents to evacuation centres, he said.

The United Nations said on Tuesday six bodies had been recovered so far and the total affected population, including those needing possible evacuation and relocation, was estimated at 7,849.

Papua New Guinea regularly experiences landslides and natural disasters that rarely make headlines, but this is one of the most devastating ones it’s seen in recent years.

The government has estimated that more than 2,000 people were buried in the landslide which occurred early Friday, sharply higher than U.N.’s figure of 670 possible deaths, and some local officials’ much lower estimates.

‘COMPLICATED’ RELIEF EFFORT

The relief operation was extraordinarily complicated, said Nicholas Booth, resident representative at the United Nations Development Programme (UNDP), with the terrain continuing to move.

“It means that now, the area that’s been affected by the landslide is greater than it was at the beginning. We don’t know how it will develop, but that’s the nature of the geology in PNG,” he said.

A long-running tribal conflict has made it harder for aid workers to access the site, Booth said. Eight people were killed and 30 houses torched in fighting on Saturday.

A total of 150 structures were estimated to have been buried by the landslide.

The UN said on Tuesday that immediate needs included clean water, food, clothing, shelter items, kitchen utensils, medicine and hygiene kits and psychosocial support.

Provincial authorities have requested the international community to send engineers to carry out a geohazard assessment, the UN said in a statement.

The differing fatality estimates reflect the difficulty in getting an accurate population count. The nation’s last credible census was in 2000 and a 2022 voter roll doesn’t include those under 18.

Such estimates should be treated with “great caution,” Booth said.

“Most people remain trapped under that rubble and it’s just not possible at this stage to make a very scientific, verified estimate,” he said. “But it’s going to be a very high number of casualties. We have to be prepared for that.”

(Reporting by Renju Jose, Lewis Jackson and Jill Gralow in Sydney; writing by Praveen Menon; Editing by Michael Perry and Bernadette Baum)

Disclaimer: This report is auto generated from the Reuters news service. ThePrint holds no responsibilty for its content.

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