Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model - Hou, Boyang; Fu, Hanjiao; Li, Xin; Song, Tao; Zhang, Zhiyuan (Frontiers in Marine Science, 2024)
The precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing. This paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the significant wave height in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The experimental results show that the SAC-ConvLSTM model has the best prediction performance compared with other models, with RMSE, MAE, and PCC of 0.2117m, 0.1083m, and 0.9630, respectively, at the 24-hour prediction. In addition, the introduction of wind can improve the accuracy of wave prediction.The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas.