Multi-Hazard Geological Susceptibility Assessment for Sustainable Disaster Risk Reduction Using Slope Units and Interpretable Machine Learning: A Case Study of Tongren City, Qinghai Province, China

Geological hazards pose persistent challenges to sustainable land use planning, infrastructure safety, and community resilience in alpine valley regions. Taking Tongren City on the northeastern margin of the Qinghai–Tibet Plateau as the study area, this study assessed composite susceptibility to landslides, rockfalls, and debris flows using 18,136 slope units, 189 historical hazard points, and 92 independent verification points, with the three hazard types combined into a single positive class for regional screening. Twelve conditioning factors were retained after collinearity testing, and negative-sample buffer distances of 200, 500, 1000, and 1500 m were compared before applying Bayesian-optimized random forest (Bo-RF), Bayesian-optimized CatBoost (Bo-CatBoost), and TabPFN models. Model performance was evaluated through repeated buffered spatial block cross-validation, probability calibration, spatial error analysis, independent verification, and SHAP interpretation. The 1000 m buffer produced the best negative-sample performance, with accuracy of 0.905 and an ROC-AUC of 0.972. Under spatial validation, the three models showed comparable discrimination, with ROC-AUC values of 0.9152–0.9161 and accuracy values of 0.8509–0.8549; Bo-CatBoost performed better in probability calibration, whereas TabPFN exhibited weaker spatial clustering of residuals. The TabPFN very-high-susceptibility zone covered 17.03% of the study area and contained 84.66% of historical hazard points, while the corresponding Bo-CatBoost zone captured 72.83% of independent verification points. SHAP analysis identified the mean annual rainfall, distance to water systems, distance to roads, NDVI, and lithology as the principal factors shaping the composite susceptibility pattern. Overall, the proposed framework provides spatial decision support for sustainable land use planning, resilient infrastructure management, targeted hazard investigation, and the efficient allocation of disaster prevention resources in alpine valley regions.