A significant wave height prediction method based on deep learning combining the correlation between wind and wind waves

Abstract

Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. The model learns the intrinsic correlations of the data generated by the numerical model, making it possible to combine the correlations between wind and wind waves to improve the predictions. In addition, this study also optimizes the long-term prediction ability of the model through the proposed Mask method and Replace mechanism. The experimental results show that the introduction of the wind field can significantly improve the significant wave height prediction results. The research on the prediction effect of the entire study area and two separate stations shows that the prediction performance of the proposed model is better than the existing methods. The model makes full use of the physical correlation between wind and wind waves, and the validity is up to 24 hours. The 24-hour forecast R² reached 0.69.