From typhoon rainfall to slope failure: optimizing susceptibility models and dynamic thresholds for landslide warnings in Zixing City, China

Abstract. From typhoon rainfall to slope failure, this study addresses the urgent need for typhoon-adapted hazard warning systems in mountainous regions like Zixing City, China. We develop an integrated framework to optimize dynamic susceptibility models and rainfall thresholds by leveraging machine learning and spatiotemporal rainfall analysis. Using buffer-based negative sampling (0.1–5.0 km) and variable weighting methods (IV, CF, FR), we compare SVM and LightGBM models. The SVM model with FR input at 0.5 km buffer achieved the highest accuracy (AUC=0.913), correctly classifying 86.4 % of landslides in high-risk zones, revealing how typhoon-driven hydrology interacts with slope instability. For rainfall thresholds, the H24-D7 model (24-hour intensity vs. 7-day antecedent rainfall) emerged as optimal (71.8 % accuracy), effectively capturing typhoon-specific triggers like short-term downpours and cumulative soil saturation. Kriging interpolation generated spatially explicit thresholds, identifying granite slopes and road-proximal areas as hotspots for typhoon-induced failures. The final hazard warning system, integrating susceptibility and dynamic thresholds, showed 71.4 % overlap with historical landslides, emphasizing the critical role of typhoon rainfall dynamics in slope failure prediction. This work provides a scalable approach for regions facing typhoon-related landslide risks, prioritizing both spatial heterogeneity and temporal rainfall patterns.