Research on Core Factor Sets for Landslide Susceptibility Mapping Based on Interpretable Machine Learning Methods

Landslides are one of the most common natural hazards in China, and the efficient screening of important factors is crucial for landslide susceptibility mapping. Taking the Zigui–Badong section of the Three Gorges Reservoir Area (TGRA) as the study area, this research initially selected 25 evaluation factors based on topography, geology, hydrology, remote sensing images, and previous studies. Thirteen key factors were obtained through analysis. Three machine learning models—RF, DT, and XGBoost—were then used for landslide susceptibility mapping, with SHAP and LIME employed to interpret the models. Finally, a scoring method was used to rank the six sets of results and compare them with those from the traditional AUC-based Recursive Feature Elimination (AUC-RFE) method. The results showed that the core factor sets screened by interpretable methods outperformed those from AUC-RFE. To further obtain accurate core factor sets, two additional interpretable methods—PI and Explainable Boosting Machine (EBM)—were integrated, ultimately identifying a core factor set consisting of eight factors including Elevation, Slope Height, and Aspect. This set achieved an AUC value of 0.931, only 0.003 lower than that of the 13 filtered factors. The screening method proposed in this paper can significantly improve the efficiency of factor acquisition, reduce the difficulty of factor acquisition, and provide a new approach for the selection of key factors in landslide susceptibility assessment.