Preprints
https://doi.org/10.5194/egusphere-2022-874
https://doi.org/10.5194/egusphere-2022-874
 
04 Oct 2022
04 Oct 2022
Status: this preprint is open for discussion.

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 Wen Zhu et al.
  • College of Environmental Science and Engineering, Tongji University, Shanghai, 200000, China

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.

Wen Zhu et al.

Status: open (until 29 Dec 2022)

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Wen Zhu et al.

Wen Zhu et al.

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Short summary
To provide a possibility for early warning and forecasting of ponding in the urban drainage system, an optimized LSTM-based model is proposed in this paper. It has a remarkable improvement as compared to the models based on LSTM and CNN structures. The performance of the corrected model is reliable if the number of monitoring sites is over one per hectare. Increasing the number of monitoring points further has little impact on the performance.