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Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Author

Listed:
  • Xun Liu

    (School of Arts and Communication, China University of Geosciences (Wuhan), Wuhan 430070, China)

  • Peng Zhou

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Yichen Lin

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Siwei Sun

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Hailu Zhang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Wanqing Xu

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

  • Sangdi Yang

    (School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430074, China)

Abstract

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.

Suggested Citation

  • Xun Liu & Peng Zhou & Yichen Lin & Siwei Sun & Hailu Zhang & Wanqing Xu & Sangdi Yang, 2022. "Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms," IJERPH, MDPI, vol. 19(24), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:24:p:16544-:d:998412
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    References listed on IDEAS

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    1. Zening Wu & Yanxia Shen & Huiliang Wang, 2019. "Assessing Urban Areas’ Vulnerability to Flood Disaster Based on Text Data: A Case Study in Zhengzhou City," Sustainability, MDPI, vol. 11(17), pages 1-15, August.
    2. Huawei Li & Guifang Wang & Guohang Tian & Sándor Jombach, 2020. "Mapping and Analyzing the Park Cooling Effect on Urban Heat Island in an Expanding City: A Case Study in Zhengzhou City, China," Land, MDPI, vol. 9(2), pages 1-17, February.
    3. José I. Barredo & Guy Engelen, 2010. "Land Use Scenario Modeling for Flood Risk Mitigation," Sustainability, MDPI, vol. 2(5), pages 1-18, May.
    4. Jingjing Luo & Shiyan Zhai & Genxin Song & Xinxin He & Hongquan Song & Jing Chen & Huan Liu & Yuke Feng, 2022. "Assessing Inequity in Green Space Exposure toward a “15-Minute City” in Zhengzhou, China: Using Deep Learning and Urban Big Data," IJERPH, MDPI, vol. 19(10), pages 1-17, May.
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    1. Chun Deng & Xiaohui Jiang & Chentian Jiang & Tong Nie & Yuxin Lei & Anle Yang, 2025. "Insights into teleconnection mechanism of extreme precipitation events based on the SHAP-XGBoost model: evidence from Hekou-Longmen section in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(6), pages 7447-7468, April.
    2. Changhong Zhou & Mu Chen & Jiangtao Chen & Yu Chen & Wenwu Chen, 2024. "A Multi-Hazard Risk Assessment Model for a Road Network Based on Neural Networks and Fuzzy Comprehensive Evaluation," Sustainability, MDPI, vol. 16(6), pages 1-16, March.

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