首页|Advancing flood susceptibility modeling using stacking ensemble machine learning:A multi-model approach

Advancing flood susceptibility modeling using stacking ensemble machine learning:A multi-model approach

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Flood susceptibility modeling is crucial for rapid flood forecasting,disaster reduction strategies,evacuation planning,and decision-making.Machine learning(ML)models have proven to be effective tools for assessing flood susceptibility.However,most previous studies have focused on individual models or comparative performance,underscoring the unique strengths and weaknesses of each model.In this study,we propose a stacking ensemble learning algorithm that harnesses the strengths of a diverse range of machine learning mod-els.The findings reveal the following:(1)The stacking ensemble learning,using RF-XGB-CB-LR model,significantly enhances flood susceptibility simulation.(2)In addition to rainfall,key flood drivers in the study area include NDVI,and impervious surfaces.Over 40％of the study area,primarily in the northeast and southeast,exhibits high flood susceptibility,with higher risks for populations compared to cropland.(3)In the northeast of the study area,heavy precipitation,low terrain,and NDVI values are key indicators contributing to high flood susceptibility,while long-duration precipitation,mountainous topography,and upper reach vegetation are the main drivers in the southeast.This study underscores the effectiveness of ML,particularly ensemble learning,in flood modeling.It identifies vulnerable areas and con-tributes to improved flood risk management.

flood susceptibility assessmentmachine learningstacking ensemble learningflood driversXiangjiang River Basin

YANG Huilin、YAO Rui、DONG Linyao、SUN Peng、ZHANG Qiang、WEI Yongqiang、SUN Shao、AGHAKOUCHAK Amir

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School of Geography and Tourism,Anhui Normal University,Wuhu 241002,Anhui,China

Changjiang River Scientific Research Institute,Wuhan 430015,China

Advanced Interdisciplinary Institute of Environment and Ecology,Beijing Normal University,Zhuhai 519087,Guangdong,China

Hunan Institute of Water Resources and Hydropower Research,Changsha 410007,China

State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China

Department of Civil and Environmental Engineering,University of California Irvine,Irvine,CA 92697,USA

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National Natural Science Foundation of ChinaKey Research and Development Program Project of Anhui ProvinceUniversity Synergy Innovation Program of Anhui ProvinceScience Foundation for Excellent Young Scholars of Anhui

422710372022m07020011GXXT-2021-0482108085Y13

2024

10.1007/s11442-024-2259-2

地理学报(英文版)
中国地理学会,中国科学院地理科学与资源研究所

地理学报(英文版)

CSTPCD
影响因子：1.307
ISSN：1009-637X
年,卷(期)：2024.34(8)
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