Significant wave height prediction based on the local-EMD-WaveNet model

Abstract

This research constructed the innovative Local-EMD-WaveNet, a multi-channel neural network model, specifically designed for the prediction of significant wave height (SWH) at a singular point. It leverages Local Empirical Mode Decomposition (EMD) on significant wave heights in Ghanaian waters, integrating the derived decomposition results with wind speed data. This compiled data is then channeled into the model, which exploits the capabilities of dilated causal convolution to capture and analyze the time-series characteristics integral to future SWH predictions. The model ingeniously embeds EMD within the training process, treating the decomposed sub-waves and wind speed sequences as unique channels along the “depth” dimension. Following the application of dilated causal convolution, these channels are systematically “stacked”. Compared to conventional LSTM and direct data incorporation methods, Local-EMD-WaveNet consistently outperforms, especially in long-term predictions. The model exhibited significant improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) during 48 and 56 time-step predictions, marking increases of 27.3% and 23.5%, respectively, outshining both WaveNet and LSTM. Particularly in situations with larger wave height variations, Local-EMD-WaveNet accurately captures waveforms' peaks and troughs. These results validate Local-EMD-WaveNet as a reliable wave forecasting tool with considerable potential in ocean engineering and maritime applications.