Impact of Different Mode Decomposition Methods Combined with LSTM Models on Daily Runoff Forecasting - TAN Yongjie; WANG Xianxun; DUAN Mingxu; LIU Yaru; YAO Huaming (Renmin Zhujiang)
A combination of modal decomposition and deep learning forecasting methods was introduced to daily runoff forecasting to address the characteristics of unstable and volatile daily runoff series.Firstly,the complete ensemble empirical modal decomposition method was used to decompose the daily runoff time series,so as to obtain the modal components of different frequency components.Secondly,the daily runoff forecasting model was constructed for different modal components based on the long short-term memory neural network (LSTM),and the hyperparameters of the forecasting model were optimized using the grid search parametric optimization algorithm.Finally,the forecasting results of each model were modally reconstructed to obtain daily runoff forecasting results.The daily runoff forecasting of the Yichang hydrological station was taken as an example.Compared with the single LSTM,the RMSE,MAE,and MAPE of the proposed combination model were reduced by 65.02%,58.35%,and 2.88%,respectively.The decomposition effect of the complete ensemble empirical mode decomposition was better than that of the traditional modal decomposition method,which provided a new method and reference for nonlinear and non-stable daily runoff forecasting in a short time scale.