A Machine Learning Framework for Regional Identification of Landslide and Debris Flow Hazard Chains

Regional identification and risk mapping of landslide and debris flow hazard chains (LDHCs) are essential for disaster prevention and land-use planning in mountainous regions. This study proposes a machine learning framework for the regional identification and risk mapping of LDHCs in southern Gansu Province, China. A spatial database was established using an inventory of 160 landslide hazard chains (LHCs) and 121 debris-flow hazard chains (DHCs), together with eight environmental conditioning factors. Considering the distinct geomorphological characteristics of the two hazard types, grid-based and watershed-based mapping units were adopted for landslide and debris-flow susceptibility mapping, respectively. Five ensemble learning algorithms were evaluated to identify the optimal susceptibility models. Hazard maps were subsequently generated by integrating susceptibility with earthquake- and extreme rainfall-triggering factors, and regional risk maps were produced by incorporating population and Gross Domestic Product (GDP) exposure data. The Random Forest model achieved the best performance for LHC susceptibility mapping, with an accuracy of 84.9% and an AUC of 0.914, whereas the Extra Trees model performed best for DHC susceptibility mapping, with an accuracy of 92.6% and an AUC of 0.978. The resulting risk maps indicate that the middle and lower reaches of the Bailong River, including Wudu, Zhouqu, Qin’an, and Tianshui, represent the highest-risk areas for LDHCs. The proposed framework provides an effective and transferable approach for regional identification and risk mapping of LDHCs, offering valuable support for disaster prevention, emergency planning, and land-use management in mountainous regions.