Research on the refined intelligent assessment of potential population risk from regional accumulation layer landslide disasters

Regional landslide risk assessment is essential for disaster prevention, yet traditional methods are limited by inadequate characterisation of conditioning factor heterogeneity in landslide susceptibility prediction (LSP) and spatial scale mismatch between administrative population data and slope units. This study develops an integrated framework combining automated slope unit division, heterogeneity quantification, and machine learning. Using Multi-Scale Segmentation in Anyuan County, 148,608 slope units were divided. Intra-unit heterogeneity for LSP was quantified through statistical features of conditioning factors. The Heterogeneous Slope-RF model incorporating heterogeneity achieved higher predictive accuracy (AUC = 0.941) than models ignoring it. For population density, an upper threshold of 120 persons/km² was applied, and a slope unit-scale RF model produced precise estimates (R² = 0.8157, MAE = 1.7 persons/km²). This refined distribution reduces overestimation of risk in urban centres and identifies dispersed settlements in high-susceptibility mountainous areas. By integrating this population distribution, landslide susceptibility, and AHP-based vulnerability, a spatial map of potential population loss risk was generated for Anyuan County. The study confirms that incorporating spatial heterogeneity and refined population modelling improves landslide risk assessment accuracy, supporting identification of genuine high-risk zones.