Risk Assessment of Multi‑Hazards in Hangzhou:A Socioeconomic and Risk Mapping Approach Using the CatBoost‑SHAP Model  

作　　者：Bofan Yu Jiaxing Yan Yunan Li Huaixue Xing 

机构地区：[1]The Institute of Geological Survey of China University of Geosciences(Wuhan),China University of Geosciences(Wuhan),Wuhan 430074,China [2]China Geological Survey,Nanjing Center,Nanjing 210016,China

出　　处：《International Journal of Disaster Risk Science》2024年第4期640-656,共17页国际灾害风险科学学报（英文版）

基　　金：supported by the China Geological Survey,Nanjing Center,Zhejiang Geological Survey,and China University of Geosciences(Wuhan);funded by the Laboratory of Geological Safety of Underground Space in Coastal Cities,Ministry of Natural Resources(Project No.BHKF2022Z02);the China Geological Survey,Nanjing Center(Project No.DD20190281).

摘　　要：As the global push for sustainable urban development progresses, this study, set against the backdrop of Hangzhou City,one of China's megacities, addressed the conflict between urban expansion and the occurrence of urban geological hazards.Focusing on the predominant geological hazards troubling Hangzhou-urban road collapse, land subsidence, and karst collapse-we introduced a Categorical Boosting-SHapley Additive exPlanations(CatBoost-SHAP) model. This model not only demonstrates strong performance in predicting the selected typical urban hazards, with area under the curve(AUC) values reaching 0.92, 0.92, and 0.94, respectively, but also, through the incorporation of the explainable model SHAP, visually presents the prediction process, the interrelations between evaluation factors, and the weight of each factor. Additionally, the study undertook a multi-hazard evaluation, producing a susceptibility zoning map for multiple hazards, while performing tailored analysis by integrating economic and population density factors of Hangzhou. This research enables urban decision makers to transcend the “black box” limitations of machine learning, facilitating informed decision making through strategic resource allocation and scheduling based on economic and demographic factors of the study area. This approach holds the potential to offer valuable insights for the sustainable development of cities worldwide.

关 键 词：Hangzhou city Multi-hazard risk assessment Machine learning Machine learning interpretability Socioeconomic analysis Urban sustainability development 

分 类 号：X4[环境科学与工程—灾害防治] P694[天文地球—地质学] TP181[自动化与计算机技术—控制理论与控制工程]