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Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model

Author

Listed:
  • Chengshuai Liu

    (Zhengzhou University)

  • Caihong Hu

    (Zhengzhou University)

  • Chenchen Zhao

    (Zhengzhou University)

  • Yue Sun

    (Zhengzhou University)

  • Tianning Xie

    (Zhengzhou University)

  • Huiliang Wang

    (Zhengzhou University)

Abstract

Accurate flood simulation has significant practical implications for urban flood management. The focus of this study is to develop a new flood model (K-SWMMG) based on the Storm Water Management Model (SWMM), which innovatively couples the K-means clustering machine learning algorithm and GIS spatial analysis techniques. The K-means clustering machine learning algorithm is used to determine the uncertain parameters of the SWMM model, while GIS spatial analysis techniques enhance the two-dimensional realism of flood simulation. We applied the K-SWMMG model to six historical observed flood events in a specific catchment area in Zhengzhou City, using rainfall and flow data. The study shows that: 1) K-SWMMG optimizes the sub-basin division method of urban stormwater models, avoiding the tedious and complex parameter calibration process, and improving modeling efficiency to some extent. 2) The two-dimensional visualization of inundation provided by GIS spatial analysis techniques better meets the production requirements of current urban flood simulation. 3) K-SWMMG outperforms SWMM in terms of simulation performance, with improvements in absolute error (AE), relative error (RE), Nash-Sutcliffe efficiency coefficient (NSE), and coefficient of determination (R2) by 0.019m, 5.36%, 0.068, and 0.042, respectively. The findings can provide scientific decision-making references for urban flood forecasting and early warning.

Suggested Citation

  • Chengshuai Liu & Caihong Hu & Chenchen Zhao & Yue Sun & Tianning Xie & Huiliang Wang, 2024. "Research on Urban Storm Flood Simulation by Coupling K-means Machine Learning Algorithm and GIS Spatial Analysis Technology into SWMM Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(6), pages 2059-2078, April.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:6:d:10.1007_s11269-024-03743-w
    DOI: 10.1007/s11269-024-03743-w
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    References listed on IDEAS

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    1. Xuan Wang & Wenchong Tian & Zhenliang Liao, 2021. "Offline Optimization of Sluice Control Rules in the Urban Water System for Flooding Mitigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 949-962, February.
    2. Zening Wu & Bingyan Ma & Huiliang Wang & Caihong Hu & Hong Lv & Xiangyang Zhang, 2021. "Identification of Sensitive Parameters of Urban Flood Model Based on Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(7), pages 2115-2128, May.
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    Cited by:

    1. Pengjun Li & Luwen Zhuang & Kairong Lin & Dunxian She & Qiuling Chen & Qiang Wang & Jun Xia, 2025. "New perspectives on urban stormwater management in China, with a focus on extreme rainfall events," 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(4), pages 3745-3774, March.

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