AUTHOR=Song Tao , Han Runsheng , Meng Fan , Wang Jiarong , Wei Wei , Peng Shiqiu 

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

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2022.983007

DOI=10.3389/fmars.2022.983007

ISSN=2296-7745

ABSTRACT=The accurate prediction of wave height is of great significance for people's production and life. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLSTM algorithm. It can combine the correlation between wind and wind waves to improve the forecast effect. 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.