Abstract: (16944 Views)
Landslides are major natural hazards which not only result in the loss of human life but also cause economic
burden on the society. Therefore, it is essential to develop suitable models to evaluate the susceptibility of slope failures
and their zonations. This paper scientifically assesses various methods of landslide susceptibility zonation in GIS
environment. A comparative study of Weights of Evidence (WOE), Analytical Hierarchy Process (AHP), Artificial
Neural Network (ANN), and Generalized Linear Regression (GLR) procedures for landslide susceptibility zonation is
presented. Controlling factors such as lithology, landuse, slope angle, slope aspect, curvature, distance to fault, and
distance to drainage were considered as explanatory variables. Data of 151 sample points of observed landslides in
Mazandaran Province, Iran, were used to train and test the approaches. Small scale maps (1:1,000,000) were used in
this study. The estimated accuracy ranges from 80 to 88 percent. It is then inferred that the application of WOE in
rating maps’ categories and ANN to weight effective factors result in the maximum accuracy.