首页|Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods
Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods
扫码查看
点击上方二维码区域,可以放大扫码查看
原文链接
万方数据
维普
Floods are widespread and dangerous natural hazards worldwide.It is essential to grasp the causes of floods to mitigate their severe effects on people and society.The key drivers of flood susceptibility in rapidly urbanizing areas can vary depending on the specific context and require further investigation.This research developed an index system comprising 10 indicators associated with factors and environments that lead to disasters,and used machine learning methods to assess flood susceptibil-ity.The core urban area of the Yangtze River Delta served as a case study.Four scenarios depicting separate and combined effects of climate change and human activity were evaluated using data from various periods,to measure the spatial vari-ability in flood susceptibility.The findings demonstrate that the extreme gradient boosting model outperformed the decision tree,support vector machine,and stacked models in evaluating flood susceptibility.Both climate change and human activity were found to act as catalysts for flooding in the region.Areas with increasing susceptibility were mainly distributed to the northwest and southeast of Taihu Lake.Areas with increased flood susceptibility caused by climate change were significantly larger than those caused by human activity,indicating that climate change was the dominant factor influencing flood suscep-tibility in the region.By comparing the relationship between the indicators and flood susceptibility,the rising intensity and frequency of extreme precipitation as well as an increase in impervious surface areas were identified as important reasons of heightened flood susceptibility in the Yangtze River Delta region.This study emphasized the significance of formulating adaptive strategies to enhance flood control capabilities to cope with the changing environment.
School of Civil Engineering and Transportation,State Key Laboratory of Subtropical Building and Urban Science,South China University of Technology,Guangzhou 510641,China
Pazhou Lab,Guangzhou 510335,China
Center for Water Resources and Environment,Sun Yat-sen University,Guangzhou 510275,China