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https://doi.org/10.5194/egusphere-2022-874
https://doi.org/10.5194/egusphere-2022-874
04 Oct 2022
 | 04 Oct 2022

An optimized LSTM-based approach applied to early warning and forecasting of ponding in the urban drainage system

Wen Zhu, Tao Tao, Hexiang Yan, Jieru Yan, Jiaying Wang, Shuping Li, and Kunlun Xin

Abstract. An optimized LSTM-based approach applied to early warning and forecasting of ponding in the urban drainage system is proposed in this study. This approach can identify locations and process of ponding quickly with relatively high accuracy. The model is constructed with two tandem processes and a multi-task learning mechanism is introduced. The results are compared with those of widely used neural networks (LSTM, CNN) to validate its advantages. Then, the model is revised with available monitoring data in the study area to achieve higher accuracy, and the influence of the number of the monitoring points selected on the performance of the corrected model is also discussed in this paper. Over 15000 designed rainfall events are used for model training, covering a diversity of extreme weather conditions.

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Journal article(s) based on this preprint

26 May 2023
An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system
Wen Zhu, Tao Tao, Hexiang Yan, Jieru Yan, Jiaying Wang, Shuping Li, and Kunlun Xin
Hydrol. Earth Syst. Sci., 27, 2035–2050, https://doi.org/10.5194/hess-27-2035-2023,https://doi.org/10.5194/hess-27-2035-2023, 2023
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

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To provide a possibility for early warning and forecasting of ponding in the urban drainage...
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