Evaluating Yangtze River Delta Urban Agglomeration flood risk using a hybrid method of automated machine learning and analytic hierarchy process
Yu Gao, Haipeng Lu, Yaru Zhang, Hengxu Jin, Shuai Wu, Yixuan Gao, Shuliang Zhang
Abstract
Abstract. With rapid urbanization, the scientific assessment of disaster risk caused by flooding events has become an essential task for disaster prevention and mitigation. However, adaptively selecting optimal machine learning (ML) models for flood risk assessment and further conducting spatial and temporal analyses of flood risk characteristics in urban agglomerations remain challenging. This study establishes an H–E–V–R risk assessment index system that integrates hazard, exposure, vulnerability, and resilience based on the factors influencing flood risk in the Yangtze River Delta Urban Agglomeration (YRDUA). Utilizing automated machine learning (AutoML) and the analytic hierarchy process (AHP), a comprehensive flood risk assessment model is constructed. Results indicate that, among the different assessment models, the accuracy, precision, F1 score, and kappa coefficient of the categorical boosting (CatBoost) model for flooded point identification are the highest. Among the flood hazard factors, elevation ranks highest in importance, with a contribution rate of up to 68.55 %. The spatial distribution of flood risk in the study area from 1990 to 2020 is heterogeneous, with an overall increasing risk trend. This study is of great significance, advancing disaster prevention, mitigation, and sustainable development in the YRDUA.