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Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique

Author

Listed:
  • Xi Yang

    (Sun Yat-sen University)

  • Zhihe Chen

    (Sun Yat-sen University
    Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai))

  • Min Qin

    (Sun Yat-sen University
    Guangdong Research Institute of Water Resources and Hydropower)

Abstract

Accurate prediction of monthly runoff is critical for optimal water resource allocation. However, previous studies mainly focused on the direct prediction of the decomposition sequence, ignoring the error accumulation and the increase in calculation time. In addition, the influence of each sequence on the prediction results was not clarified. Therefore, this study proposes a hybrid prediction method combining time varying filtering-based empirical mode decomposition (TVF-EMD), permutation entropy (PE), a long short-term memory model (LSTM) and a particle swarm algorithm (PSO). Firstly, TVF-EMD is applied for decomposing the original runoff sequences to obtain different components; secondly, PE is applied for characterizing the complexity of different components and reconstructing similar components to obtain new components; then, the decomposed-reconstructed runoff data are predicted by using the LSTM model with PSO based on the analytical studies of different watersheds. The outcomes indicate that the performance index of the proposed model is better than that of the comparison model, improving the prediction accuracy effectively. In addition, the impact of each subseries on prediction performance was also investigated in this study. These findings indicate that the developed model has potential application prospects in runoff prediction and can provide scientific support for water conservancy project operations.

Suggested Citation

  • Xi Yang & Zhihe Chen & Min Qin, 2024. "Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 269-286, January.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03668-w
    DOI: 10.1007/s11269-023-03668-w
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    References listed on IDEAS

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    1. Jincheng Zhou & Dan Wang & Shahab S. Band & Changhyun Jun & Sayed M. Bateni & M. Moslehpour & Hao-Ting Pai & Chung-Chian Hsu & Rasoul Ameri, 2023. "Monthly River Discharge Forecasting Using Hybrid Models Based on Extreme Gradient Boosting Coupled with Wavelet Theory and Lévy–Jaya Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 3953-3972, August.
    2. Mehdi Jamei & Mumtaz Ali & Anurag Malik & Ramendra Prasad & Shahab Abdulla & Zaher Mundher Yaseen, 2022. "Forecasting Daily Flood Water Level Using Hybrid Advanced Machine Learning Based Time-Varying Filtered Empirical Mode Decomposition Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4637-4676, September.
    3. Wen-chuan Wang & Yu-jin Du & Kwok-wing Chau & Dong-mei Xu & Chang-jun Liu & Qiang Ma, 2021. "An Ensemble Hybrid Forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4695-4726, November.
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