Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning

Jinyu Meng, Zengchuan Dong (), Yiqing Shao, Shengnan Zhu and Shujun Wu
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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

Sustainability, 2022, vol. 15, issue 1, 1-15

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.

Keywords: ensemble learning; interval sliding window; least absolute shrinkage and selection operator; monthly runoff forecasting; time series (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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