基于BPNN-SHAP模型的滑坡危险性评价:以伊犁河流域为例OA北大核心CSTPCD

To further improve the accuracy of landslide hazard prediction models and enhance their interpretability,this study selected 8 influencing factors of landslide occurrence,taking the Yili River Basin,Xinjiang province as an example.An interpretable BPNN-SHAP model,based on the back propagation neural network(BPNN)model and the game theory with the aim of addressing the'black box'issue,was constructed.Firstly,the dataset was divided into 70%training set and 30%test set,and 5-fold cross-validation was used to enhance the robustness of the BPNN-SHAP model.Then,the evaluation accuracy of this model was compared with three other models:Deep Neural Network(DNN),Random Forest(RF),and Logistic Regression(LR).Finally,regional landslide hazard assessment was completed,and the interpretability of BPNN-SHAP was also discussed.The results showed that the BPNN-SHAP model achieved the highest statistical values in the following metrics:Accuracy(A)=0.904,Precision(P)=0.911,Recall(R)=0.919,F1Score=0.915,and SAUC=0.905.The very high and high danger areas for landslides in the study region accounted for 11.96%and 15.53%,respectively.Among these regions,Xinyuan and Nileke County occupy the highest proportions,at approximately 51.1%and 45.6%,respectively.The primary controlling factors for landslides were elevation,slope,rainfall,and peak ground acceleration(PGA).Specifically,areas with an elevation of 1 500 m to 2 000 m,slopes greater than 14°,annual rainfall between 260 mm and 310 mm,and PGA greater than 0.23 g are prone to landslides,indicating that the predominant types of landslides are rainfall-induced and earthquake-induced.Our research method is expected to provide a new technical reference for landslide hazard assessment and theoretical support for disaster prevention,mitigation,and resilience construction in the Yili River Basin.