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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年3月11日

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  • Zaichao Xie,
  • Wei Xu,
  • Bing Zhu,
  • Shiming Yin,
  • Yi Yang,
  • Xiaojie Li,
  • Sufan Wang

摘要

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.