A significant wave height prediction method with ocean characteristics fusion and spatiotemporal dynamic graph modeling

Accurate significant wave height (SWH) prediction is essential for the development and utilization of wave energy. Deep learning methods such as recurrent and convolutional neural networks have achieved good results in SWH forecasting. However, these methods do not adapt well to dynamic seasonal variations in wave data. In this study, we propose a novel method—the spatiotemporal dynamic graph (STDG) neural network. This method predicts the SWH of multiple nodes based on dynamic graph modeling and multi-characteristic fusion. First, considering the dynamic seasonal variations in the wave direction over time, the network models wave dynamic spatial dependencies from long- and short-term pattern perspectives. Second, to correlate multiple characteristics with SWH, the network introduces a cross-characteristic transformer to effectively fuse multiple characteristics. Finally, we conducted experiments on two datasets from the South China Sea and East China Sea to validate the proposed method and compared it with five prediction methods in the three categories. The experimental results show that the proposed method achieves the best performance at all predictive scales and has greater advantages for extreme value prediction. Furthermore, an analysis of the dynamic graph shows that the proposed method captures the seasonal variation mechanism of the waves.