Sea Surface Temperature Prediction Based on Spatio-Temporal Graph Contrastive Learning Network

There is a growing interest in using spatio-temporal graph networks to predict sea surface temperature (SST). Nevertheless, numerous existing methodologies struggle to accurately capture both local and global spatial dependencies and are red frequently vulnerable to data noise. To tackle these challenges, we introduce a novel framework known as Spatio-Temporal Graph Contrastive Learning Network (STGLN), which enhances the precision of sea surface temperature forecasts. Notably, STGLN employs both feature-level and topology-level data augmentation techniques to alleviate the influence of data noise. Subsequently, the refined SST feature matrix and adjacency matrix are analyzed through a dual-branch network with shared parameters, specifically designed to effectively capture both local and global spatial dependencies within the SST time series data. Furthermore, we propose an auxiliary task for spatio-temporal contrastive learning, which aims to mitigate the sparsity of supervised signals and to efficiently extract essential spatio-temporal information. Comprehensive experiments conducted on real-world SST datasets demonstrate that STGLN significantly surpasses other advanced methodologies.