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A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model

Author

Listed:
  • Shunqing Jia

    (Tongji University)

  • Xihua Wang

    (Tongji University
    University of Waterloo)

  • Y. Jun Xu

    (Louisiana State University Agricultural Center)

  • Zejun Liu

    (Tongji University)

  • Boyang Mao

    (Tongji University)

Abstract

Accurate forecasting of mid to long-term runoff is essential for water resources management. However, the traditional model cannot predict well and the precision of runoff forecast needs to be further improved. Here, we proposed a novel data-driven model aimed at enhancing the performance of the Gated Recurrent Unit (GRU) through the integration of Robust Local Mean Decomposition (RLMD) and the Slime Mould Algorithm (SMA). The objective is to improve mid to long-term runoff prediction in three hydrographic stations: Heishiguan, Baimasi, and Longmenzhen, located within the Yiluo River Watershed in central China. The model leverages monthly runoff data spanning from 2007 to 2022 to achieve this objective. The results indicated that (1) the new data-driven model (RLMD -SMA-GRU) had the highest monthly runoff prediction accuracy. Both RLMD and SMA can improve the accuracy of the model (NSE = 0.9466). (2) The precision of the models in wet season outperformed in dry season. (3) The hydrological stations with large discharge and stable runoff sequence have better forecasting effect. The RLMD-SMA-GRU model has good applicability and can be applied to the monthly runoff forecast at watershed scale.

Suggested Citation

  • Shunqing Jia & Xihua Wang & Y. Jun Xu & Zejun Liu & Boyang Mao, 2024. "A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(13), pages 5179-5194, October.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:13:d:10.1007_s11269-024-03907-8
    DOI: 10.1007/s11269-024-03907-8
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    References listed on IDEAS

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    1. Omid Bozorg-Haddad & Mahboubeh Zarezadeh-Mehrizi & Mehri Abdi-Dehkordi & Hugo A. Loáiciga & Miguel A. Mariño, 2016. "A self-tuning ANN model for simulation and forecasting of surface flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2907-2929, July.
    2. Yutao Qi & Zhanao Zhou & Lingling Yang & Yining Quan & Qiguang Miao, 2019. "A Decomposition-Ensemble Learning Model Based on LSTM Neural Network for Daily Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4123-4139, September.
    3. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
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