Monthly Water Level Prediction Based on ESMD-VMD-ESN Hybrid Model - LI Ang; ZHANG Kun; SANG Yuting; BI Wan (Renmin Zhujiang)
Water level sequence contain complex features of multiple frequency information.To improve the prediction accuracy of the water level sequences,a combined model was developed based on Extreme-point Symmetric Mode Decomposition (ESMD),Variational Mode Decomposition (VMD) and Echo State Network (ESN),namely ESMD-VMD-ESN.And it was applied to forecast water level of the Taipuzha station in the upper reaches of Taipu River.The predictive effect of the “first decomposition-second decomposition-prediction-reconstruction” model was explored by comparing it with a single model ESN and the combination model ESMD-ESN.The results show that ESMD-VMD-ESN has the highest accuracy,followed by ESMD-ESN,and the lowest ESN accuracy.Compared with the ESN,the Willmott's Index of Agreement (WIA) and Pearson Correlation Coefficient (PCC) of ESMD-ESN respectively increased by 51% and 11%,the Mean Absolute Error (MAE) and Root Mean Squard Error (RMSE) of ESMD-ESN respectively decreased by 14% and 45%.ESMD can effectively simplify the water level sequence and reduce the prediction error.Compared with the ESMD-ESN,the WIA and PCC of ESMD-VMD-ESN respectively increased by 5% and 10%,the MAE and RMSE of ESMD-ESN respectively decreased by 52% and 50%.VMD can further simplify the highest frequency component of ESMD and improving the model prediction accuracy.In conclusion,the combined model ESMD-VMD-ESN has well applicability and stability in the monthly water level prediction.