Coupling Time and Non-Time Series Models to Simulate the Flood Depth at Urban Flooded Area

Hongfa Wang, Xinjian Guan, Yu Meng, Zening Wu, Kun Wang and Huiliang Wang ()
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Hongfa Wang: Zhengzhou University
Xinjian Guan: Zhengzhou University
Yu Meng: Zhengzhou University
Zening Wu: Zhengzhou University
Kun Wang: China Institute of Water Resources and Hydropower Research
Huiliang Wang: Zhengzhou University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 3, No 14, 1275-1295

Abstract: Abstract The flood produced by short duration heavy rainfall events in cities will still exist after raining and continues to cause harm and impact. To accurately predict the depth and duration of the flood, a coupled model of the extreme gradient boosting and long short-term memory algorithms was proposed. A practical application of three representative flooded points in the Zhengzhou city, China, the results showed the coupled model could fit and forecast the flood. The average of Mean relative error, Nash–Sutcliffe efficiency coefficient and Qualified rate of validation data were 9.13%, 0.96 and 90.3% respectively, which verified the superiority of the method in the flood prediction. And the flood processes at the flooded points caused by design rainfall under different return periods were predicted by the coupled model. The growth rates of the flood duration and peak flood depth were all the highest during the return periods 1a-2a. This study proves that the coupled model has great potential in predictions of flood and could provide scientific basis guidance for disaster reduction.

Keywords: Flood process; XGBoost model; Dynamic time warping algorithm; LSTM model; Multiple rainfall scenarios (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03430-2

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