Discharge Prediction in Tidal Reach Using Harmonic Analysis and VMD-BP Neural Network

  Objectives  In view of the low accuracy of flow forecast in tidal reach due to the combined influence of runoff and tide, combining the ability of variational mode decomposition (VMD) to process non-stationary signals and the advantages of back propagation (BP) neural network to deal with nonlinear problems, this paper proposes a new method for discharge prediction in tidal reach based on the combination model of tidal current harmonic analysis and VMD-BP neural network.

  Methods  First, the harmonic analysis method is used to separate the tidal discharge and residual discharge from the original discharge. Second, a BP neural network is constructed based on error backpropagation algorithm. Then, the BP neural network is used to simulate and train the power flow data and residual current data processed by VMD. Finally, the output tidal discharge and residual discharge components are superimposed and reconstructed to obtain the final discharge prediction results.

  Results  Experiments performed at the Xuliujing section of the Yangtze Estuary show that the discharge prediction accuracy of BP neural network method is better than that of traditional harmonic analysis method, with the root mean square error (RMSE) is decreased by about 3 400 m³/s, and the relative standard accuracy (RSD) is increased by about 6%. In addition, compared with the traditional method, RMSE of the proposed method in this paper is decreased by about 5 000 m³/s, and the RSD is increased by about 9%.

  Conclusions  The combined model based on harmonic analysis and VMD-BP neural network can effectively improve the prediction accuracy of discharge in tidal reach. At the same time, it also provides a new idea for the discharge prediction in the waters with variable flow conditions.