Short-term Prediction of the Significant Wave Height and Average Wave Period based on VMD-TCN-LSTM Algorithm

Abstract. The present work proposes a prediction model of significant wave height (SWH) and average wave period (APD) based on variational mode decomposition (VMD), temporal convolutional networks (TCN), and long short-term memory (LSTM) networks. The wave sequence features were obtained using VMD technology based on the wave data from the National Data Buoy Center. Then the SWH and APD prediction models were established using TCN, LSTM, and Bayesian hyperparameter optimization. The VMD-TCN-LSTM model was compared with the VMD-LSTM (without TCN cells) and LSTM (without VMD and TCN cells) models. The VMD-TCN-LSTM model has significant superiority and shows robustness and generality in different buoy prediction experiments. In the 3-hour wave forecasts, VMD primarily improved the model performance, while the TCN had less influence. In the 12-, 24-, and 48-hour wave forecasts, both VMD and TCN improved the model performance. The contribution of the TCN to the improvement of the prediction result determination coefficient gradually increased as the forecasting length increased. In the 48-hour SWH forecasts, the VMD and TCN improved the determination coefficient by 132.5 % and 36.8 %, respectively. In the 48-hour APD forecasts, the VMD and TCN improved the determination coefficient by 119.7 % and 40.9 %, respectively.