Monthly Runoff Forecasting Based on Interval Sliding Window and Ensemble Learning

被引:5
|
作者
Meng, Jinyu [1 ]
Dong, Zengchuan [1 ]
Shao, Yiqing [1 ]
Zhu, Shengnan [1 ]
Wu, Shujun [1 ]
机构
[1] Hohai Univ, Coll Hydrol & Water Resources, Nanjing 210098, Peoples R China
关键词
ensemble learning; interval sliding window; least absolute shrinkage and selection operator; monthly runoff forecasting; time series; MODEL; PREDICTION;
D O I
10.3390/su15010100
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:15
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