GIS-based landslide susceptibility mapping using analytical hierarchy process (AHP) and certainty factor (CF) models for the Baozhong region of Baoji City, China

Abstract

The main purpose of this study was to map landslide susceptibility through the AHP and CF models, using a geographic information system (GIS), for the Baozhong region of Baoji City, China. At first, a landslide inventory map was prepared using technical reports, aerial photographs, and coupling with field surveys. A total of 79 landslides were mapped, out of which 55 (70 %) were randomly selected for building landslide susceptibility models, while the rest 24 landslides (30 %) were applied for validating the models. In this case study, the following landslide conditioning factors were evaluated: slope degree, slope aspect, plan curvature, altitude, geomorphology, lithology, distance from faults, distance from rivers, and precipitation. Subsequently, landslide susceptibility maps were produced using the AHP and CF models. Finally, the validation of landslide susceptibility map was accomplished with areas under the curve (AUC) and the Seed Cell Area Index (SCAI). The AUC plot estimation results indicated that the susceptibility map applying CF model has a higher prediction accuracy of 81.43 % than the accuracy of 75.97 % applying AHP model. Similarly, the validation results also showed that the success rate of the CF model was 85.93 %, while the success rate was 77.80 % for the AHP model. According to the validation results of the AUC evaluation, the map produced by CF model behaves better performance. Furthermore, the validation results using the SCAI also indicated that the CF model has a higher predication accuracy than the AHP model. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation.