Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins

Zaichao Xie, Wei Xu (), Bing Zhu, Shiming Yin, Yi Yang, Xiaojie Li and Sufan Wang
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Zaichao Xie: Chongqing Jiaotong University
Wei Xu: Chongqing Jiaotong University
Bing Zhu: MWR
Shiming Yin: Qingshen County Water Conservancy Bureau
Yi Yang: Chongqing Jiaotong University
Xiaojie Li: Chongqing Jiaotong University
Sufan Wang: Chongqing Jiaotong University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 9, No 13, 4493-4507

Abstract: 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.

Keywords: Transfer learning; LSTM; K-nearest neighbor; SWAT; Hydrological modeling (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04165-y

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