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

Chengshuai Liu, Caihong Hu (), Chenchen Zhao (), Yue Sun (), Tianning Xie and Huiliang Wang
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Chengshuai Liu: Zhengzhou University
Caihong Hu: Zhengzhou University
Chenchen Zhao: Zhengzhou University
Yue Sun: Zhengzhou University
Tianning Xie: Zhengzhou University
Huiliang Wang: Zhengzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 6, No 12, 2059-2078

Abstract: 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.

Keywords: Urban flood forecast; K-SWMMG; Uncertainty parameters; City functional area; K-means clustering algorithm; GIS (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03743-w

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