Simulation of Urban Flood Process Based on a Hybrid LSTM-SWMM Model

Chenchen Zhao (), Chengshuai Liu (), Wenzhong Li (), Yehai Tang (), Fan Yang (), Yingying Xu (), Liyu Quan () and Caihong Hu ()
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Chenchen Zhao: Zhengzhou University
Chengshuai Liu: Zhengzhou University
Wenzhong Li: Zhengzhou University
Yehai Tang: Zhengzhou University
Fan Yang: Zhengzhou University
Yingying Xu: Zhengzhou University
Liyu Quan: Zhengzhou University
Caihong Hu: Zhengzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 13, No 11, 5187 pages

Abstract: Abstract This study proposes a novel hybrid LSTM-SWMM model that integrates the advantages of the SWMM model and the LSTM neural network for the first time. The aim is to build an efficient and rapid model that considers the physical mechanism, in order to effectively simulate urban floods. The results indicate a good agreement between the simulated discharge process of the LSTM-SWMM model and the observed discharge process during the training and testing periods, reflecting the actual rainfall runoff process. The $${R}^{2}$$ R 2 of the LSTM-SWMM model is 0.969, while the $${R}^{2}$$ R 2 of the LSTM model is 0.954. Additionally, for a forecasting period of 1, the $$NSE$$ NSE value of the LSTM-SWMM model is 0.967, representing the highest forecasting accuracy. However, for a forecasting period of 6, the $$NSE$$ NSE value of the LSTM-SWMM model decreases to 0.939, indicating lower accuracy. As the forecasting period increases, the $$NSE$$ NSE values consistently decrease, leading to a gradual decrease in accuracy.

Keywords: LSTM; SWMM; Urban Flood; Hydrologic elements (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03600-2

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