A Hybrid Monthly Runoff Prediction Model Based on SGMD-SE-AVOA-LSTM
The medium-and long-term runoff time series are strongly nonlinear and non-stationary,so it is difficult to conduct model prediction accurately.Therefore,combining symplectic geometric mode decomposition(SGMD),sample entropy(SE),African vulture optimization algorithm(AVOA),and long-and short-term memory neural network(LSTM),this paper proposes a new hybrid model,SGMD-SE-AVOA-LSTM.This paper first uses SGMD and SE to preprocess data.After that,the paper makes use of AVOA to optimize the hyperparameters of LSTM.Finally,by superimposing the predicted results of each sequence,the paper obtains the predicted value of the monthly runoff.The monthly runoff data of Yingluoxia Hydrological Station in the Heihe River Basin and Hongjiadu Hydropower Station in the Wujiang River Basin are selected for example validation and compared with the LSTM model,AVOA-LSTM model,EEMD-LSTM model,SGMD-SE-LSTM model,and EEMD-AVOA-LSTM model.The results show that the NSE and R of the SGMD-SE-AVOA-LSTM model at Yingluoxia Hydrological Station are as high as 0.8961 and 0.9498,respectively.Compared with the comparison model,the MAE decreases by 45.26％,18.95％,26.33％,20.09％,and 14.07％,respectively;the RMSE decreases by 40.06％,25.74％,31.24％,19.24％,and 21.65％,respectively;and the NSE and R of the SGMD-SE-AVOA-LSTM model at Hongjiadu Hydropower Station are as high as 0.7949 and 0.8935,respectively.Compared with the comparison model,the MAE decreases by 39.87％,17.86％,27.61％,20.48％,and 13.58％,respectively;the RMSE decreases by 39.08％,29.10％,31.86,15.11％,and 22.66％,respectively.Therefore,the model proposed in this article effectively enhances the prediction accuracy of the LSTM model and provides a new hybrid model for monthly runoff prediction.
monthly runoff predictionsymplectic geometric mode decompositionsample entropyafrican vulture optimization algorithmneural networks for long and short-term memoryhybrid modelHeihe River BasinWujiang River Basin
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