Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

Kaili Zhu; Zhaoli Wang; Chengguang Lai; Shanshan Li; Zhaoyang Zeng; Xiaohong Chen

doi:10.1007/s13753-024-00590-6

Volume 15 Issue 5

Oct. 2024

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Article Navigation > International Journal of Disaster Risk Science > 2024 > 15(5): 738-753

Kaili Zhu, Zhaoli Wang, Chengguang Lai, Shanshan Li, Zhaoyang Zeng, Xiaohong Chen. Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods[J]. International Journal of Disaster Risk Science, 2024, 15(5): 738-753. doi: 10.1007/s13753-024-00590-6

Citation:

Kaili Zhu, Zhaoli Wang, Chengguang Lai, Shanshan Li, Zhaoyang Zeng, Xiaohong Chen. Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods[J]. International Journal of Disaster Risk Science, 2024, 15(5): 738-753. doi: 10.1007/s13753-024-00590-6

Citation:

Kaili Zhu, Zhaoli Wang, Chengguang Lai, Shanshan Li, Zhaoyang Zeng, Xiaohong Chen. Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods[J]. International Journal of Disaster Risk Science, 2024, 15(5): 738-753. doi: 10.1007/s13753-024-00590-6

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Evaluating Factors Affecting Flood Susceptibility in the Yangtze River Delta Using Machine Learning Methods

doi: 10.1007/s13753-024-00590-6

1. School of Civil Engineering and Transportation, State Key Laboratory of Subtropical Building and Urban Science, South China University of Technology, Guangzhou, 510641, China;
2. Pazhou Lab, Guangzhou, 510335, China;
3. Center for Water Resources and Environment, Sun Yat-sen University, Guangzhou, 510275, China

Funds:

This research is financially supported by the National Natural Science Foundation of China (Grant Nos. 52209019, 52379010), the Natural Science Foundation of Guangdong Province (Grant Nos. 2023B1515020087, 2022A1515240071), the Fund of Science and Technology Program of Guangzhou (2023A04J1595), and the Open Research Fund of Key Laboratory of Water Security Guarantee in the Guangdong-Hong Kong-Marco Greater Bay Area of Ministry of Water Resources (WSGBAKJ2023010).

Accepted Date: 2024-10-10

Available Online: 2024-12-07

Publish Date: 2024-10-28

Abstract

Abstract

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 susceptibility. 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 variability 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 susceptibility 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.
- Climate change,
- Flood susceptibility,
- Human activity,
- Machine learning methods,
- Yangtze River Delta core urban agglomeration

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References(69)

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