Quanhong Zheng

Landslides are among the most common and destructive geological hazards in complex mountainous regions. Developing accurate and interpretable susceptibility assessment models is important for regional geological hazard prevention and spatial safety management. To address the limitations of traditional machine learning models in processing tabular geoscience data with limited samples and complex nonlinear relationships, this study took Qingchuan County, Sichuan Province, as the study area and introduced the Tabular Prior-data Fitted Network (TabPFN) for landslide susceptibility assessment. Its performance was systematically compared with four commonly used models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). An evaluation system was built using 12 conditioning factors, including elevation, lithology, and distance to roads (DTR). The SHapley Additive exPlanations (SHAP) method was further used to identify the contributions and effects of the main controlling factors. The results showed that TabPFN achieved the highest observed predictive performance among the evaluated models, with an AUC of 0.7834, showing higher observed performance than traditional models such as LR, SVM, RF, and XGBoost, indicating its strong ability to capture nonlinear relationships and good applicability under limited sample conditions. SHAP analysis further indicated that elevation, lithology, and distance to roads were the main controlling factors influencing the model predictions. This study demonstrates the applicability of TabPFN for landslide susceptibility assessment in complex mountainous regions and provides a new methodological reference for intelligent geological hazard prediction and refined regional risk management under limited data conditions.