Landslide Susceptibility Screening for Regional Investigation Prioritization: Integrating Ensemble Learning, Spatial Validation, Model Agreement, and Probability Uncertainty

Regional landslide susceptibility screening can support sustainable land management by improving the allocation of limited geological-survey and field-verification resources toward locations requiring further investigation. An uncertainty-aware framework integrating ensemble learning, spatial validation, inter-model agreement, and probability uncertainty was proposed for regional landslide investigation prioritization in the Hualong–Xunhua region of the Upper Yellow River Basin, China. Using 281 manually interpreted landslide locations, 281 pseudo-absence samples, and twelve conditioning factors, four tree-based models (Random Forest, XGBoost, LightGBM, and CatBoost) were developed, and their probability outputs were combined through arithmetic averaging. Model performance was evaluated using stratified random validation and 10 km spatial block validation, while agreement and probability divergence were incorporated to define screening priorities. Random-validation AUC values ranged from 0.885 to 0.898, and spatial-validation AUC values ranged from 0.852 to 0.873; the Mean Ensemble achieved AUC values of 0.8961 and 0.8637, respectively. NDVI showed the highest mean normalized permutation importance (0.754), although sensitivity analysis demonstrated that other factors retained useful discrimination after removing NDVI and land-cover information. The final priority screening zone covered 862.72 km2 (19.04% of the valid mapped area). The framework provides a transparent decision-support approach for sustainable land management in mountainous regions by identifying investigation priorities while accounting for model consistency and uncertainty.