Adaptive Spatiotemporal Graph Recurrent Network for Sea Surface Temperature Forecasting

Sea surface temperature (SST) serves as a crucial indicator for balancing marine energy levels and assessing the thermal content of ocean waters. The diverse effects prompted by oceanic temperature fields significantly impact human activities on a large scale. Efficient and accurate prediction of SST is of great significance. However, due to the complex spatial and temporal relationships between sea areas, how to model the spatial information of sea areas is a challenging problem. In this article, we propose an adaptive spatiotemporal graph recurrent network (ASTGRN) model to capture the hidden spatial corrections between sea areas for SST forecasting. First, we propose an adaptive node representation learning (ANRL) module to capture the trend of temperature changes in different sea areas. Second, we use a graph structure learning (GSL) module to learn the dynamic spatial corrections between different sea areas without predefined graph adjacency matrices. Finally, the prediction results are obtained through a spatiotemporal recurrent network. Experimental evaluations of the selected data from the South China Sea (SCS), Bohai Sea, and the East China Sea (ESC) demonstrate the superiority of the proposed ASTGRN compared with state-of-the-art baselines in SST forecasting.