# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
###### Abstract  
The evaluation of landslide susceptibility plays a crucial role in preventing the risks associated with landslides and debris flows, providing valuable insights for the effective prevention and mitigation of geological hazards. However, there is limited research on high-altitude areas. Therefore, this study chose the western Tibetan Plateau as the study area, a representative area known for its susceptibility to landslides and high attitudes. In this study, seven factors were identified based on research objectives. Information value (IVM), weight of evidence (WOE), information value logistic regression (IVM-LR), weight of evidence logistic regression (WOE-LR), information value multi-layer perceptron (IVM-MLP) and weight of evidence multi-layer perceptron (WOE-MLP) were selected and compared for landslide susceptibility. The percentage of disaster area included in each risk level, the AUC value and the ROC curve were used to evaluate the accuracy of the results. The ROC curves of the results were close to the upper-left corner and the AUC values exceeded 0.85, an indication that all results were highly accurate. Moreover, the percentage of disaster area included for each risk showed an upward trend regarding susceptibility. The results indicated that the hybrid model exhibited superior performance in assessing landslide susceptibility at high altitudes. Overall, the results showed great significance regarding disaster prevention and mitigation measures of local governments.  
information model; weight of evidence; logistic regression; multi-layer perceptron; landslide susceptibility 202020202448520244852024485202448520244852024485202448520244852024485202448520244852024485202448520244852024485202448520244852024852024485202448520244852024485202448520248520248520244852 models is relatively weak, and machine learning models need sufficient and balanced data. There is no best single or mixed model that can be used in all areas. High-altitude areas have less interference from human factors and provide a good demonstration area for the comparative analysis of different models. At the same time, the roads in these areas are relatively simple; however, disasters may cause great damage to local traffic, and so they have extremely important practical research significance. In addition, limited research has been conducted in high-altitude areas of the Tibetan Plateau.  
Based on this context, the present study selected Zhipuqi, which has the lowest altitude (3869 m) in the region, as the study area (Figure 1). Landslides represent one of the most significant geological hazards in this area. Analyzing landslide susceptibility mapping has considerable practical implications for planning and disaster prevention, significantly enhancing the safety of lives and property. To assess the feasibility of geological hazard analysis in high-altitude areas and to compare model differences, we employed the IVM [36], the weight of evidence (WoE) method [37; 38; 39; 40], the LR model [41; 42; 43; 44; 45] and the multi-layer perceptron (MLP) method [46; 47; 48]. Of these models, the IVM and WoE can more quickly obtain a stable weight of each low-cost factor, and the LR and MLP can provide a higher learning ability for mixed models.