A Conditional Probability-Based Model for Mountainous Geological Hazard Susceptibility Assessment

The occurrence of mountainous geological hazards, primarily including rockfalls, landslides, and debris flows, is frequently influenced by multiple environmental factors and exhibits significant spatial heterogeneity and cumulative effects. To address the need for regional-scale susceptibility assessments within complex geological settings, we propose a novel geological hazard susceptibility assessment model based on conditional probability. This study establishes a dual-module evaluation framework incorporating certainty factors (CFs) and weights (W), in which the CF quantifies the contribution of each factor class to hazard occurrence, while the weights reflect the relative importance of the conditioning factors, thereby improving the model’s capability to characterize multifactorial coupling effects. Using three representative mountainous regions in Xinjiang, China—the Ili Valley Region (IVR), the Northern Piedmont of the Tianshan Mountains (NPTM), and the Kunlun–Altun Mountain Region (KAMR)—we integrate 7938 historical hazard points and 11 conditioning factors within a GIS environment to conduct the assessment. The results reveal regional differences in the weights of conditioning factors: IVR is primarily controlled by Elevation (0.184), Urban-Critical Infrastructure Density (0.163), and Annual Precipitation (0.156); NPTM is dominated by Annual Precipitation (0.153), Urban-Critical Infrastructure Density (0.145), and Road Density (0.136); and KAMR is governed by Elevation (0.197), Seismic Acceleration (0.167), and Hydrogeological Type (0.134). In IVR, NPTM, and KAMR, the Very-High and High susceptibility zones occupy 37.10%, 34.86%, and 26.23% of the land area, respectively, and contain 78.18%, 77.24%, and 82.10% of the identified geological hazards. The region-specific ROC-AUCs are 0.8536 (IVR), 0.8545 (NPTM), and 0.8775 (KAMR), indicating good predictive capability across sedimentary basins and tectonically active zones. This study provides methodological and data support for quantitative risk assessment of geological hazards at the regional scale under complex geological conditions.