Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition

Abstract

Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is proposed. Firstly, CEEMDAN is used to decompose the original data, and the high-frequency component is decomposed using VMD. Then, each component is input into the LSTM optimized by CABES for prediction. Finally, the results of individual component predictions are combined and reconstructed to produce the monthly runoff predictions. The hybrid model is employed to predict the monthly runoff at the Xiajiang hydrological station and the Yingluoxia hydrological station. A comprehensive comparison is conducted with other models including back propagation (BP), LSTM, etc. The assessment of each model's prediction performance uses four evaluation indexes. Results reveal that the CEEMDAN-VMD-CABES-LSTM model showcased the highest forecast accuracy among all the models evaluated. Compared with the single LSTM, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the Xiajiang hydrological station decreased by 71.09 and 65.26%, respectively, and the RMSE and MAPE of the Yingluoxia hydrological station decreased by 65.13 and 40.42%, respectively. The R and NSEC of both sites are near 1. HIGHLIGHTS A novel model (CEEMDAN-VMD-CABES-LSTM) is proposed for monthly runoff prediction.; CEEMDAN-VMD effectively reduces the complexity of the original monthly runoff series.; CABES enhances the generalization ability and prediction performance of the LSTM.; The four evaluation indicators and seven benchmark models are employed to verify the superiority of the developed model.;

Keywords

• complete ensemble emd (ceemdan)
• hybrid model
• long short-term memory network (lstm)
• monthly runoff prediction
• secondary decomposition
• variational mode decomposition (vmd)