Landslide Susceptibility Mapping Based on Interpretable Machine Learning from the Perspective of Geomorphological Differentiation

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective topography differentiation. (2) Methods: This selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as corrosion layered high middle mountain region (Zone I), (Wulong, Pengshui Shizhu southeastern mountainous strong karst gorges II), area. used a Bayesian optimization algorithm optimize parameters LightGBM XGBoost models construct evaluation for each two regions. model with accuracy selected according indicators order establish mapping. SHAP then explore formation mechanisms different landforms both global local perspective. (3) Results: AUC values test set mode Zones I II are 0.8525 0.8859, respectively, those 0.8214 0.8375, respectively. shows that has prediction regard landforms. Under landform types, elevation, land use, incision depth, distance road average annual rainfall were common dominant factors contributing most decision making at sites; fault river have degrees influence under types. (4) Conclusions: optimized LightGBM-SHAP is suitable analysis types landscapes, namely region, gorges, can be internal decision-making mechanism levels, which makes results more realistic transparent. beneficial selection index system early prevention control hazards, provide reference potential hazard-prone areas research.