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

Author

Listed:
  • Zaichao Xie

    (Chongqing Jiaotong University)

  • Wei Xu

    (Chongqing Jiaotong University
    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)

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.

Suggested Citation

  • Zaichao Xie & Wei Xu & Bing Zhu & Shiming Yin & Yi Yang & Xiaojie Li & Sufan Wang, 2025. "Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 39(9), pages 4493-4507, July.
    • Handle: RePEc:spr:waterr:v:39:y:2025:i:9:d:10.1007_s11269-025-04165-y
    DOI: 10.1007/s11269-025-04165-y
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    References listed on IDEAS

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    1. You-Da Jhong & Chang-Shian Chen & Bing-Chen Jhong & Cheng-Han Tsai & Song-Yue Yang, 2024. "Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(3), pages 1141-1164, February.
    2. Anbang Peng & Xiaoli Zhang & Wei Xu & Yuanyang Tian, 2022. "Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2381-2394, May.
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