首页|A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector-machine learning coupled model
A comprehensive framework for assessing the spatial drivers of flood disasters using an Optimal Parameter-based Geographical Detector-machine learning coupled model
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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)mod-els were utilized to construct the OPGD-RFE-LGBM coupled model to identify the essential driving fac-tors 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 distribu-tion 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 dis-aster inventory of 7332 historical events was prepared,and 22 potential driving factors related to precip-itation,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 driving factors and quantitatively analyzing their impacts.Moreover,the simulation performance shows a slight improvement(a 6％average decrease in RMSE and an average increase of 1％in R2)even with reduced factor data.Factor explanatory analysis indicated that the combination of the essential driving factor sets varied across different subregions;nev-ertheless,precipitation-related factors,such as precipitation intensity index(SDII),wet days(R10MM),and 5-day maximum precipitation(RX5day),were the main driving factors controlling flood disasters.This study provides a quantitative analytical framework for the spatial drivers of flood disasters at large scales with significant heterogeneity,offering a reference for disaster management authorities in devel-oping macro-strategies for disaster prevention.
Flood disasterSpatial driving factorsSpatial heterogeneityMachine learningOptimal Parameter-based GeographicalDetectorYunnan Province
Luyi Yang、Xuan Ji、Meng Li、Pengwu Yang、Wei Jiang、Linyan Chen、Chuanjian Yang、Cezong Sun、Yungang Li
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Institute of International Rivers and Eco-security,Yunnan University,Kunming 650500,China
Yunnan Key Laboratory of International Rivers and Transboundary Eco-security,Yunnan University,Kunming 650500,China
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