Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins
Hydrological models have made significant advances in methodologies and applications in recent years. However, there is still a need to address the challenge of modeling in areas with limited or no data. This study proposes a transferred Long Short-Term Memory (T-LSTM) network based on transfer lear...
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| Vydáno v: | Water resources management Ročník 39; číslo 9; s. 4493 - 4507 |
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01.07.2025
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| ISSN: | 0920-4741, 1573-1650 |
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| Abstract | Hydrological models have made significant advances in methodologies and applications in recent years. However, there is still a need to address the challenge of modeling in areas with limited or no data. This study proposes a transferred Long Short-Term Memory (T-LSTM) network based on transfer learning and Long Short-Term Memory (LSTM) networks to address this issue. Firstly, the K-nearest neighbor (K-NN) algorithm is used to estimate precipitation data, while the Soil and Water Assessment Tool (SWAT) is applied to generate long series of flow data for training. Secondly, four transfer learning scenarios, classified into intra-basin transfer and inter-basin transfer, are constructed based on the simulated and observed data. Finally, T-LSTM networks are constructed with different transfer learning scenarios and the performance of the networks is evaluated in five river basins in China, Hunjiang, Jialingjiang, Wujiang, Minjiang and Jinshajiang. The results indicate that inter-basin T-LSTM networks perform exceptionally well in data-scarce basins, particularly those with similar hydrometeorological and basin characteristics. |
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| AbstractList | Hydrological models have made significant advances in methodologies and applications in recent years. However, there is still a need to address the challenge of modeling in areas with limited or no data. This study proposes a transferred Long Short-Term Memory (T-LSTM) network based on transfer learning and Long Short-Term Memory (LSTM) networks to address this issue. Firstly, the K-nearest neighbor (K-NN) algorithm is used to estimate precipitation data, while the Soil and Water Assessment Tool (SWAT) is applied to generate long series of flow data for training. Secondly, four transfer learning scenarios, classified into intra-basin transfer and inter-basin transfer, are constructed based on the simulated and observed data. Finally, T-LSTM networks are constructed with different transfer learning scenarios and the performance of the networks is evaluated in five river basins in China, Hunjiang, Jialingjiang, Wujiang, Minjiang and Jinshajiang. The results indicate that inter-basin T-LSTM networks perform exceptionally well in data-scarce basins, particularly those with similar hydrometeorological and basin characteristics. |
| Author | Xie, Zaichao Zhu, Bing Yin, Shiming Wang, Sufan Xu, Wei Yang, Yi Li, Xiaojie |
| Author_xml | – sequence: 1 givenname: Zaichao surname: Xie fullname: Xie, Zaichao organization: College of River and Ocean Engineering, Chongqing Jiaotong University – sequence: 2 givenname: Wei orcidid: 0000-0002-2226-4096 surname: Xu fullname: Xu, Wei email: xuwei19850711@163.com organization: College of River and Ocean Engineering, Chongqing Jiaotong University, National Engineering Research Center for Inland Waterway Regulation, Chongqing Jiaotong University – sequence: 3 givenname: Bing surname: Zhu fullname: Zhu, Bing organization: Information Center (Hydrology Monitor and Forecast Center), MWR – sequence: 4 givenname: Shiming surname: Yin fullname: Yin, Shiming organization: Qingshen County Water Conservancy Bureau – sequence: 5 givenname: Yi surname: Yang fullname: Yang, Yi organization: College of River and Ocean Engineering, Chongqing Jiaotong University – sequence: 6 givenname: Xiaojie surname: Li fullname: Li, Xiaojie organization: College of River and Ocean Engineering, Chongqing Jiaotong University – sequence: 7 givenname: Sufan surname: Wang fullname: Wang, Sufan organization: College of River and Ocean Engineering, Chongqing Jiaotong University |
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| Cites_doi | 10.1109/TCPMT.2020.3043011 10.1016/j.fuel.2024.131461 10.1016/j.apenergy.2024.122685 10.1016/j.eswa.2024.124139 10.1016/j.jhydrol.2024.131215 10.1029/1999WR900028 10.1016/j.atmosenv.2019.116885 10.1016/0022-1694(70)90255-6 10.1016/j.jhydrol.2021.126573 10.1007/s11269-024-04052-y 10.1109/CVPR.2018.00131 10.1007/s11269-022-03148-7 10.1118/1.4957255 10.1162/neco.1997.9.8.1735 10.2166/nh.2020.026 10.1007/s11600-023-01157-7 10.1111/j.1752-1688.1998.tb05961.x 10.1007/s11269-023-03713-8 10.1109/ISSCC.2017.7870350 10.1016/j.rineng.2023.101566 10.3390/w12010096 10.1016/j.ejrh.2024.101744 |
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| SubjectTerms | Algorithms Artificial intelligence Atmospheric Sciences Basins China Civil Engineering Earth and Environmental Science Earth Sciences Environment Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrologic data Hydrologic models Hydrology Hydrology/Water Resources Hydrometeorology Interbasin transfers K-nearest neighbors algorithm Long short-term memory Machine learning Networks Neural networks Performance evaluation Precipitation River basins River flow River forecasting Rivers Soil and Water Assessment Tool model Stream flow Time series Topography Transfer learning water Water resources |
| Title | Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins |
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