Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China)

2016 ◽  
Vol 75 (7) ◽  
Author(s):  
Qiqing Wang ◽  
Wenping Li ◽  
Yanli Wu ◽  
Yabing Pei ◽  
Peng Xie
2018 ◽  
Vol 56 (1) ◽  
pp. 19-30
Author(s):  
Prakash Gyawali ◽  
Naresh Kazi Tamrakar

Landslide susceptibility analysis is carried out in the Chure Khola Catchment, between Amlekhganj and the Churia Mai Range of the Bara District, covering area of 20 sq. km. The catchment lies in the Siwalik Hills composing the Siwalik Group of rocks of Middle Miocene to Early Pleistocene age. Owing to the week and fragile geology, the Siwalik Hills are prone to the gully erosion, shallow landslide and debris flow, during the heavy rain storms in monsoon seasons. In the present study, landslide susceptibility assessment was carried out using two methods, rapid field-based assessment and statistical index methods. For the susceptibility mapping of the river bank slopes, field- based method was used. The seven parameters such as slope angle, slope material, reduction to groundwater, effect of drainage, effect of past failure, effect of vegetation cover and effect of land use were used to calculate the factor of safety in the field. The slope areas were classified as highly susceptible (FS<0.7), susceptible (0.7<FS<1), marginally stable (1<FS<1.2) and stable (FS>1.2) categories, and finally, a susceptibility map was prepared. For the total 4.179 sq. km area where rapid field-based assessment was carried out, the areas covered by highly susceptible, susceptible, marginally stable and stable zones are respectively, 21.56%, 22.11%, 17.37% and 38.95%. Among the highly susceptible and susceptible zones identified, 71% sites have experienced recent slope failures. Landslide susceptibility mapping of the whole catchment area was prepared using statistical index method, and considering seven causative parameters such as elevation, slope, slope aspect, curvature, river proximity, stream density and lithology, which were determined and prepared from DEM using Arc GIS. Eighty percent landslides were used as the training sample for the spatial analysis, whereas 20% landslides were used for the validation of the study. The landslide susceptibility map exhibits the areas covered by very high, high, moderate, low and very low susceptibility zones are 47.18%, 25.28%, 19.77%, 3.60% and 4.16%, respectively. Validity of the study was determined using Riemann Sums method. Success Rate Curve shows that 78.04 % of the areas lie under the curve. Evaluating susceptibility in small watershed is important to mitigate shallow landslide related problems and in rehabilitating forest areas in the Churiya Hills of Nepal.


2018 ◽  
Vol 10 (10) ◽  
pp. 1527 ◽  
Author(s):  
Dieu Tien Bui ◽  
Himan Shahabi ◽  
Ataollah Shirzadi ◽  
Kamran Chapi ◽  
Mohsen Alizadeh ◽  
...  

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments.


Author(s):  
Gökhan Demir

Abstract. Abstract: In the present study, landslide susceptibility assessment for the the part of the North Anatolian Fault Zone is made using index of entropy models within geographical information system. At first, the landslide inventory map was prepared in the study area using earlier reports, aerial photographs and multiple field surveys. 63 cases (69 %) out of 91 detected landslides were randomly selected for modeling, and the remaining 28 (31 %) cases were used for the model validation. The landslide-trigerring factors, including slope degree, aspect, elevation, distance to faults, distance to streams, distance to road. Subsequently, landslide susceptibility maps were produced using frequency ratio and index of entropy models. For verification, the receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) calculated. The verification results showed that frequency ratio model (AUC = 75.71 %) performed slightly better than index of entropy (AUC = 75.43 %) model. The interpretation of the susceptibility map indicated that distance to streams, distance to road and slope degree play major roles in landslide occurrence and distribution in the study area. The landslide susceptibility maps produced from this study could assist planners and engineers for reorganizing and planning of future road construction.


Sign in / Sign up

Export Citation Format

Share Document