GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models

Abstract

This study aimed to produce landslide susceptibility maps using frequency ratio (FR) and evidential belief function (EBF) models based on GIS for Gongliu County, China. For this purpose, a detailed landslide inventory map was prepared and 12 landslide conditioning factors were considered; these factors were as follows: slope angle, slope aspect, curvature, plan curvature, profile curvature, altitude, the normalized difference vegetation index, rainfall, the topographic wetness index (STI), distance from roads, distance from rivers, and lithology. The landslide inventory map was prepared from published sources and aerial photographs, supported by field work. A total of 233 landslides were identified and mapped in the study area. Out of which, 70 % landslides were applied for establishing the model and 30 % landslides were used to validate the model. GIS software was used to analyze landslide conditioning factors and to map landslide susceptibility. Landslide susceptibility maps were prepared using FR and EBF models based on ArcGIS 10.0 and classified into five susceptibility zones: very low, low, moderate, high, and very high. Finally, in order to validate the accuracy of the landslide susceptibility maps produced from two models, the area under curve approach was applied. The validation results showed that the maps using FR and EBF models have a success rate of 82.41 and 79.69 %, respectively. Similarly, the maps using FR and EBF models have a predictive rate of 77.26 and 68.79 %, respectively. Based on the results, both landslide susceptibility maps produced by the two models have a high accuracy and will be helpful for land-use planning in the study area.