Significant wave height prediction based on dynamic graph neural network with fusion of ocean characteristics

Abstract

Significant wave height (SWH) is one of the core parameters for wave and accurate prediction of SWH is of great importance for ocean resource assessment. In this paper, we propose a new multi-characteristic and multi-node SWH prediction model(MCMN). The model considers the lead–lag effect among ocean characteristics and utilizes time lag correlation to automatically learn advanced indication information. For the temporal features, temporal correlations are extracted from high-dimensional spatial features efficiently in parallel using Temporal Convolutional Network(TCN). Additionally, the dependencies between nodes are modeled as the joint result of stable long-term patterns and dynamic short-term patterns. To obtain these dependencies, we introduce a novel dynamic graph neural network. Compared to previous SWH predictions focused solely on individual nodes, this model allows us to more fully explore the spatio-temporal dependencies between the nodes by capturing both long-term and short-term spatio-temporal relationship patterns among the nodes. Experiments were conducted with 120 nodes in the South China Sea and East China Sea, respectively. The results show that the model provides reliable predictions. Finally, we compare with five deep learning models, and the results show that our model has better performance in multi-node and multi-step SWH prediction.