Research on Monthly Runoff Prediction Method Based on LCD-SSA-BiLSTM Model
Runoff prediction plays an important role in the optimal allocation of water resources and flood control and drought relief.To solve the problem of large prediction errors caused by non-smoothness and extreme values of runoff se-ries and improve the prediction accuracy,this paper proposes a combined prediction model(LCD-SSA-BiLSTM)based on local characteristic-scale decomposition(LCD),sparrow search algorithm(SSA)and bi-directional long short-term mem-ory(BiLSTM)to study the monthly runoff series of four stations in the upper reaches of Fenhe River(Fenhe Reservoir Station,Shangjingyou Station,Lancun Station and Zhaishang Station).Nash efficiency coefficient(NNSE),mean abso-lute error(MMAE),root mean square error(RRMSE),and qualification rate(QQR)are used to quantitatively evaluate the prediction results.Compared with the single BiLSTM model,EMD-BiLSTM model,LCD-BiLSTM model and EMD-SSA-BiLSTM model,the results show that the LCD-SSA-BiLSTM model has higher prediction accuracy with MMAE of 10.346×104-124.629×104 m3,RRMSE of 19.416×104-191.284×104m3,NNSE of 0.975-0.988,and the QQR of all four hydrological stations were 90%and above,and the prediction accuracy was grade A.Thus,the LCD-SSA-BiLSTM mod-el is an effective method to predict non-stationary monthly runoff series.
upper reaches of the Fenhe RiverBiLSTM modelLCDmonthly runoff prediction
NETL
NSTL
万方数据
维普
