A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector–machine learning coupled model

作者：Luyi Yang, Xuan Ji, Meng Li, Pengwu Yang, Wei Jiang, Linyan Chen, Chuanjian Yang, Cezong Sun, Yungang Li · 发表于：Geoscience Frontiers · 年份：2024 · DOI：10.1016/j.gsf.2024.101889 · 被引用次数：53 · 研究领域：Flood Risk Assessment and Management、Disaster Management and Resilience、Tropical and Extratropical Cyclones Research

Flood disasters pose serious threats to human life and property worldwide. Exploring the spatial drivers of flood disasters on a macroscopic scale is of great significance for mitigating their impacts. This study proposes a comprehensive framework for integrating driving-factor optimization and interpretability, while considering spatial heterogeneity. In this framework, the Optimal Parameter-based Geographic Detector (OPGD), Recursive Feature Estimation (RFE), and Light Gradient Boosting Machine (LGBM) models were utilized to construct the OPGD–RFE–LGBM coupled model to identify the essential driving factors and simulate the spatial distribution of flood disasters. The SHapley Additive ExPlanation (SHAP) interpreter was employed to quantitatively explain the driving mechanisms behind the spatial distribution of flood disasters. Yunnan Province, a typical mountainous and plateau area in Southwest China, was selected to implement the proposed framework and conduct a case study. For this purpose, a flood disaster inventory of 7332 historical events was prepared, and 22 potential driving factors related to precipitation, surface environment, and human activity were initially selected. Results revealed that flood disasters in Yunnan Province exhibit high spatial heterogeneity, with geomorphic zoning accounting for 66.1% of the spatial variation in historical flood disasters. The OPGD–RFE–LGBM coupled model offers clear advantages over a single LGBM in identifying essential drivin...