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Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study

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  • Hao Yang

    (Lanzhou University)

  • Weide Li

    (Lanzhou University
    Lanzhou University)

Abstract

Accurate and reliable runoff prediction is essential for water resources management. In this paper, a hybrid model STL-VMD-SFO-ESN which combines seasonal adjustment method (STL), variational mode decomposition (VMD), echo state network (ESN) and sailed fish optimizer (SFO) is proposed for daily runoff prediction. Daily runoff data from three different runoff monitoring stations in China’s Yellow River basin is used to evaluate the performance of proposed model and other newly reported models. The results indicates that: (1) The proposed model performs significantly better than the traditional data-driven models and some newly reported models. (2) STL decomposition can effectively remove the seasonal component of runoff and improve modeling accuracy. (3) ESN has a strong potential in runoff prediction, and its performance can be greatly improved by using bio-optimization algorithms. Thus, this new model has strong potential for runoff prediction for further application.

Suggested Citation

  • Hao Yang & Weide Li, 2023. "Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 557-581, January.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:1:d:10.1007_s11269-022-03389-6
    DOI: 10.1007/s11269-022-03389-6
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    References listed on IDEAS

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    1. Qi Ouyang & Wenxi Lu, 2018. "Monthly Rainfall Forecasting Using Echo State Networks Coupled with Data Preprocessing Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(2), pages 659-674, January.
    2. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    3. Akram Rahbar & Ali Mirarabi & Mohammad Nakhaei & Mahdi Talkhabi & Maryam Jamali, 2022. "A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 589-609, January.
    4. Zhang, G. Peter & Qi, Min, 2005. "Neural network forecasting for seasonal and trend time series," European Journal of Operational Research, Elsevier, vol. 160(2), pages 501-514, January.
    5. Manish Kumar & Ahmed Elbeltagi & Chaitanya B. Pande & Ali Najah Ahmed & Ming Fai Chow & Quoc Bao Pham & Anuradha Kumari & Deepak Kumar, 2022. "Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2201-2221, May.
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    Cited by:

    1. Jinping Zhang & Dong Wang & Yuhao Wang & Honglin Xiao & Muxiang Zeng, 2023. "Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3377-3394, July.
    2. S. Khorram & N. Jehbez, 2023. "A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4097-4121, August.

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