A Stacking Ensemble Model of Various Machine Learning Models for Daily Runoff Forecasting

被引:50
作者
Lu, Mingshen [1 ,2 ]
Hou, Qinyao [1 ,2 ]
Qin, Shujing [1 ,2 ]
Zhou, Lihao [1 ,2 ]
Hua, Dong [3 ]
Wang, Xiaoxia [1 ,4 ]
Cheng, Lei [1 ,2 ]
机构
[1] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn Sc, Wuhan 430072, Peoples R China
[2] Hubei Prov Collaborat Innovat Ctr Water Resources, Wuhan 430072, Peoples R China
[3] Minist Water Resources, Informat Ctr, Beijing 100053, Peoples R China
[4] Dept Water Resources Hainan Prov, Haikou 570100, Peoples R China
基金
中国国家自然科学基金;
关键词
daily runoff forecasting; machine learning; stacking model; attentional mechanism; ARTIFICIAL-INTELLIGENCE; PREDICTION; IDENTIFICATION; PERFORMANCE; REGRESSION; SELECTION; BASIN; RIVER;
D O I
10.3390/w15071265
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Improving the accuracy and stability of daily runoff prediction is crucial for effective water resource management and flood control. This study proposed a novel stacking ensemble learning model based on attention mechanism for the daily runoff prediction. The proposed model has a two-layer structure with the base model and the meta model. Three machine learning models, namely random forest (RF), adaptive boosting (AdaBoost), and extreme gradient boosting (XGB) are used as the base models. The attention mechanism is used as the meta model to integrate the output of the base model to obtain predictions. The proposed model is applied to predict the daily inflow to Fuchun River Reservoir in the Qiantang River basin. The results show that the proposed model outperforms the base models and other ensemble models in terms of prediction accuracy. Compared with the XGB and weighted averaging ensemble (WAE) models, the proposed model has a 10.22% and 8.54% increase in Nash-Sutcliffe efficiency (NSE), an 18.52% and 16.38% reduction in root mean square error (RMSE), a 28.17% and 18.66% reduction in mean absolute error (MAE), and a 4.54% and 4.19% increase in correlation coefficient (r). The proposed model significantly outperforms the base model and simple stacking model indicated by both the Friedman test and the Nemenyi test. Thus, the proposed model can produce reasonable and accurate prediction of the reservoir inflow, which is of great strategic significance and application value in formulating the rational allocation and optimal operation of water resources and improving the breadth and depth of hydrological forecasting integrated services.
引用
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页数:19
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