Predictive Performance and Resampling-Based Prediction Uncertainty of a Stacking Ensemble for Landslide Susceptibility Assessment in Bayi District, China

Landslide susceptibility mapping (LSM) based on ensemble learning is commonly evaluated using deterministic metrics, whereas prediction reliability and resampling-based prediction uncertainty remain insufficiently explored. This study assessed whether a heterogeneous Stacking ensemble model was associated with higher predictive performance and greater spatial stability of susceptibility predictions in Bayi District, Tibet. Six machine-learning models and a two-layer Stacking model were trained using 12 conditioning factors and evaluated through 100 bootstrap iterations with out-of-bag (OOB) validation and leave-one-township-out cross-validation (LOTO-CV). The Stacking model yielded the highest observed predictive performance (OOB AUC = 0.978; LOTO-CV AUC = 0.9216) and was associated with lower uncertainty, with 70.85% of the study area classified as low-uncertainty (SD < 0.05). Among the 370 confirmed landslides, approximately 60% were located in areas characterized by high susceptibility and low uncertainty. SHAP analysis consistently identified distance to roads, slope, and elevation as dominant conditioning factors across models. The results suggest that predictive accuracy and stability should be jointly considered in ensemble-based LSM, because higher predictive performance was not always associated with lower prediction uncertainty.