Research on landslide hazard assessment based on improved analytic hierarchy process optimizing multiple rainfall indicators

Rainfall is the key factor that leads to landslide, so this study introduces multiple rainfall indexes to optimize the rainfall model in view of the single evaluation index of the rainfall model for landslide hazard assessment. In this study, Xiangxiang City of Hunan Province was selected as the study area, and eight types of susceptibility assessment factors including slope, aspect, elevation, normalized vegetation index (NDVI), road, fault, lithology and land use were extracted. By analyzing the characteristics of local rainfall, six types of rainfall induced assessment factors were selected for hazard assessment of the study area. The two types of evaluation factors were substituted into the improved AHP and RF combined weighting models respectively to obtain the susceptibility zoning map and rainfall induced model of the study area, and finally superimposed to obtain the hazard zoning map of the study area.Using ROC curve and hazard zoning in the studied area test results, the results show that:The AUC value of the multi-rainfall index is 17.7% higher than that of the single rainfall index, and the AUC value of the improved AHP is 6% higher than that of the traditional AHP method. It is verified by the disaster points on the day of extreme rainfall in the study area, and the actual occurrence of the disaster points is basically consistent with the hazard evaluation and zoning of the multi-rainfall index. Therefore, the rainfall model of landslides is optimized by using multiple rainfall indexes, which significantly improves the rationality of landslide hazard assessment.The study of multiple rainfall induced indicators can fill the knowledge gap in the current field, provide new insights and understanding for the field, and provide assistance for predicting and preventing landslides in related areas.