SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China

For landslide prevention and control, it is essential to establish a landslide susceptibility prediction framework that can explain the model’s decision-making process. Wushan County, Chongqing was selected as the study area, and seventeen landslide conditioning factors were initially chosen for this investigation. GeoDetector was used to remove noise factors and reduce the latitude of the data. The research investigates the use of three machine learning methods for assessing landslide susceptibility: SVM, RF, and XGBoost, and finally explains the decision mechanism of the model by SHAP-PDP. The results indicate that XGBoost has better evaluation results than RF and SVM. And XGBoost uncertainty is lower. The integrated interpretation framework based on SHAP-PDP can evaluate and interpret landslide susceptibility models both globally and locally, which is of great practical significance for the application of machine learning in landslide prediction.