3-D Ocean Temperature Prediction via Graph Neural Network With Optimized Attention Mechanisms

Ocean temperature prediction plays crucial roles in the ocean-related fields. The graph neural networks (GNNs) show advantages for modeling complex environmental issues. However, the prior methods typically focus on node features to predict ocean temperature. Note that the edge features are essential, especially for 3-D ocean temperature prediction. In this letter, a GNN with optimized attention mechanisms (GNN-OAMs) model is proposed to predict 3-D ocean temperature. The GNN-OAM introduces a random forest (RF) module to capture the nonstationary temporal dependencies. Especially, the OAMs, via combination of multiple adjacency matrices construct edge features, are presented to capture the dynamic spatial dependencies. The prediction performance of GNN-OAM model is evaluated in the 3-D ocean temperature experimental dataset, and the results show that the GNN-OAM achieves ocean temperature prediction with high accuracy, and the MAEs are 0.146 and 0.26, when predicting temperatures in next one day and five days.