IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i1d10.1007_s11269-023-03668-w.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03668-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-023-03668-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Seyedeh Hadis Moghadam & Parisa-Sadat Ashofteh & Hugo A. Loáiciga, 2022. "Optimal Water Allocation of Surface and Ground Water Resources Under Climate Change with WEAP and IWOA Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3181-3205, July.
    2. Jingwei Huang & Hui Qin & Yongchuan Zhang & Dongkai Hou & Sipeng Zhu & Pingan Ren, 2023. "Short-term Prediction Method of Reservoir Downstream Water Level Under Complicated Hydraulic Influence," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4475-4490, September.
    3. Icen Yoosefdoost & Abbas Khashei-Siuki & Hossein Tabari & Omolbani Mohammadrezapour, 2022. "Runoff Simulation Under Future Climate Change Conditions: Performance Comparison of Data-Mining Algorithms and Conceptual Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1191-1215, March.
    4. Shuai Liu & Hui Qin & Guanjun Liu & Yang Xu & Xin Zhu & Xinliang Qi, 2023. "Runoff Forecasting of Machine Learning Model Based on Selective Ensemble," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4459-4473, September.
    5. Morteza Pakdaman & Iman Babaeian & Zohreh Javanshiri & Yashar Falamarzi, 2022. "European Multi Model Ensemble (EMME): A New Approach for Monthly Forecast of Precipitation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 611-623, January.
    6. Mohammad Ehtearm & Hossein Ghayoumi Zadeh & Akram Seifi & Ali Fayazi & Majid Dehghani, 2023. "Predicting Hydropower Production Using Deep Learning CNN-ANN Hybridized with Gaussian Process Regression and Salp Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3671-3697, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:38:y:2024:i:1:d:10.1007_s11269-023-03668-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.