Integrated Monthly Runoff Prediction Model Based on Runoff Characteristic Decomposition
Revealing the regular characteristics in the chaotic runoff sequence significantly enhances the interpretabili-ty and accuracy of predicting runoff.In addressing the periodic and trend features of medium to long-term runoff se-quences,the observed monthly runoff data from the Wujiadu station in the Hongze Lake basin were collected during the years 1959 to 2019.Runoff periodic components and trend components were extracted.Based on the runoff characteristics of each component,the Extreme Gradient Boosting(XGBoost)prediction model,aligning with the rules of physical char-acteristics,was chosen for trend component prediction.The Long Short-Term Memory neural network(LSTM),known for its proficiency in capturing chaotic patterns,was selected for residual component prediction.A prediction model,in-tegrating XGBoost and LSTM and based on runoff characteristic decomposition,was constructed.This model was em-ployed to forecast monthly runoff sequences at the Wujiadu station in the Hongze Lake basin.The predicted results were compared with single prediction models such as XGBoost,LSTM,Random Forest,and BP.The results indicate that the predictive accuracy of the XGBoost-LSTM ensemble model,based on characteristic component extraction,surpasses that of single runoff prediction models.It can utilize the regular characteristics of runoff sequences,fully exploit the advanta-ges of the prediction model,and effectively improve the accuracy of runoff prediction.
runoff characteristic decompositionXGBoostLSTMintegration modelmid-long term runoff forecasting
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