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Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning

Author

Listed:
  • Jinyu Meng

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Zengchuan Dong

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Yiqing Shao

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Shengnan Zhu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

  • Shujun Wu

    (College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China)

Abstract

In recent years, machine learning, a popular artificial intelligence technique, has been successfully applied to monthly runoff forecasting. Monthly runoff autoregressive forecasting using machine learning models generally uses a sliding window algorithm to construct the dataset, which requires the selection of the optimal time step to make the machine learning tool function as intended. Based on this, this study improved the sliding window algorithm and proposes an interval sliding window (ISW) algorithm based on correlation coefficients, while the least absolute shrinkage and selection operator (LASSO) method was used to combine three machine learning models, Random Forest (RF), LightGBM, and CatBoost, into an ensemble to overcome the preference problem of individual models. Example analyses were conducted using 46 years of monthly runoff data from Jiutiaoling and Zamusi stations in the Shiyang River Basin, China. The results show that the ISW algorithm can effectively handle monthly runoff data and that the ISW algorithm produced a better dataset than the sliding window algorithm in the machine learning models. The forecast performance of the ensemble model combined the advantages of the single models and achieved the best forecast accuracy.

Suggested Citation

  • Jinyu Meng & Zengchuan Dong & Yiqing Shao & Shengnan Zhu & Shujun Wu, 2022. "Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:100-:d:1010519
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    References listed on IDEAS

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    1. Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
    2. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    3. Huiting Zheng & Jiabin Yuan & Long Chen, 2017. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation," Energies, MDPI, vol. 10(8), pages 1-20, August.
    4. Huaping Huang & Zhongmin Liang & Binquan Li & Dong Wang & Yiming Hu & Yujie Li, 2019. "Combination of Multiple Data-Driven Models for Long-Term Monthly Runoff Predictions Based on Bayesian Model Averaging," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3321-3338, July.
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