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

Luyi Yang; Xuan Ji; Meng Li; Pengwu Yang; Wei Jiang; Linyan Chen; Chuanjian Yang; Cezong Sun; Yungang Li

Geoscience Frontiers (Nov 2024)

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

Luyi Yang,
Xuan Ji,
Meng Li,
Pengwu Yang,
Wei Jiang,
Linyan Chen,
Chuanjian Yang,
Cezong Sun,
Yungang Li

Affiliations

Luyi Yang: 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
Xuan Ji: 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; Corresponding author at: Institute of International Rivers and Eco-security, Yunnan University, Kunming 650500, China..
Meng Li: Yunnan Provincial Climate Center, Kunming 650034, China
Pengwu Yang: Yunnan Provincial Climate Center, Kunming 650034, China
Wei Jiang: 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
Linyan Chen: 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
Chuanjian Yang: 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
Cezong Sun: 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
Yungang Li: 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

Journal volume & issue: Vol. 15, no. 6
p. 101889

Abstract

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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) 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 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; nevertheless, 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 developing macro-strategies for disaster prevention.

Published in Geoscience Frontiers

ISSN: 1674-9871 (Print); 2588-9192 (Online)
Publisher: Elsevier
Country of publisher: Netherlands
LCC subjects: Science: Geology
Website: https://www.sciencedirect.com/journal/geoscience-frontiers

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