Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed
ABSTRACT The fast‐flowing watershed is characterized by rapid runoff and confluence, posing challenges for accurate flood prediction. We introduce three flood forecasting model structures, namely GRU‐ED, LSTM‐FED, and LSTM‐DSA to address this issue. Through application research in three representati...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Flood Risk Management |
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| Online Access: | https://doi.org/10.1111/jfr3.70036 |
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| _version_ | 1849339549556146176 |
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| author | Fan Wang Jie Mu Cheng Zhang Weiqi Wang Wuxia Bi Wenqing Lin Dawei Zhang |
| author_facet | Fan Wang Jie Mu Cheng Zhang Weiqi Wang Wuxia Bi Wenqing Lin Dawei Zhang |
| author_sort | Fan Wang |
| collection | DOAJ |
| description | ABSTRACT The fast‐flowing watershed is characterized by rapid runoff and confluence, posing challenges for accurate flood prediction. We introduce three flood forecasting model structures, namely GRU‐ED, LSTM‐FED, and LSTM‐DSA to address this issue. Through application research in three representative watersheds, we found that: First, as input information attenuates, the predictive ability of the models may decline with an extended lead time. The incorporation of a feedback mechanism effectively addresses this issue, resulting in an average 5% improvement in Nash efficiency and a significant 26.4% reduction in the interquartile range of relative peak error. Second, the performance of the model is influenced by various factors, including the watershed characteristics, sample size, and temporal resolution. Further investigation is required to determine the extent of their influence. The attention mechanism dynamically assigns weights to input data, significantly improving model performance, especially for larger catchments. This leads to an average increase in Nash efficiency of approximately 7.86% and a reduction in the interquartile range of relative peak error by about 30.7%. Finally, the proposed models demonstrate a high level of accuracy in flood forecasting within a specific lead time, offering an innovative deep learning‐based solution to the problem of fast‐flowing watershed flood forecasting. |
| format | Article |
| id | doaj-art-48f6a5ae12fd45deb1e6de8e495c773d |
| institution | Kabale University |
| issn | 1753-318X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Flood Risk Management |
| spelling | doaj-art-48f6a5ae12fd45deb1e6de8e495c773d2025-08-20T03:44:06ZengWileyJournal of Flood Risk Management1753-318X2025-03-01181n/an/a10.1111/jfr3.70036Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing WatershedFan Wang0Jie Mu1Cheng Zhang2Weiqi Wang3Wuxia Bi4Wenqing Lin5Dawei Zhang6National Key Laboratory of Water Disaster Prevention Hohai University Nanjing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaState Key Laboratory of Simulation and Regulation of Water Cycle in River Basin Beijing P. R. ChinaABSTRACT The fast‐flowing watershed is characterized by rapid runoff and confluence, posing challenges for accurate flood prediction. We introduce three flood forecasting model structures, namely GRU‐ED, LSTM‐FED, and LSTM‐DSA to address this issue. Through application research in three representative watersheds, we found that: First, as input information attenuates, the predictive ability of the models may decline with an extended lead time. The incorporation of a feedback mechanism effectively addresses this issue, resulting in an average 5% improvement in Nash efficiency and a significant 26.4% reduction in the interquartile range of relative peak error. Second, the performance of the model is influenced by various factors, including the watershed characteristics, sample size, and temporal resolution. Further investigation is required to determine the extent of their influence. The attention mechanism dynamically assigns weights to input data, significantly improving model performance, especially for larger catchments. This leads to an average increase in Nash efficiency of approximately 7.86% and a reduction in the interquartile range of relative peak error by about 30.7%. Finally, the proposed models demonstrate a high level of accuracy in flood forecasting within a specific lead time, offering an innovative deep learning‐based solution to the problem of fast‐flowing watershed flood forecasting.https://doi.org/10.1111/jfr3.70036data‐driven modelfast‐flowing watershedflood forecastingLSTM networks |
| spellingShingle | Fan Wang Jie Mu Cheng Zhang Weiqi Wang Wuxia Bi Wenqing Lin Dawei Zhang Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed Journal of Flood Risk Management data‐driven model fast‐flowing watershed flood forecasting LSTM networks |
| title | Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed |
| title_full | Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed |
| title_fullStr | Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed |
| title_full_unstemmed | Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed |
| title_short | Deep Learning Model for Real‐Time Flood Forecasting in Fast‐Flowing Watershed |
| title_sort | deep learning model for real time flood forecasting in fast flowing watershed |
| topic | data‐driven model fast‐flowing watershed flood forecasting LSTM networks |
| url | https://doi.org/10.1111/jfr3.70036 |
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