A spatio-temporal predictive learning model for efficient sea surface temperature forecasting

Sea surface temperature (SST) significantly influences the dynamics of the global climate system, impacting climate change, marine ecosystems, and marine engineering. Traditional SST prediction methods, such as time series and machine learning models, often focus solely on temporal features and neglect spatial distribution patterns. In contrast, current deep learning techniques typically limit predictions to short-term periods. This paper introduces a novel SST prediction model that integrates both temporal and spatial dimensions, employing parallel prediction and a spatio-temporal attention mechanism to enhance accuracy. The model achieves long-term SST forecasting, significantly reduces the parameter count and computational effort, and maintains high prediction precision. Experiments in the El Ni & ntilde;o 3.4 region and the East China Sea show that this method outperforms existing deep learning approaches, accurately predicting SST over periods ranging from 7 to 60 days with superior efficiency and accuracy. Overall, this work presents an effective new approach for SST prediction with crucial implications for climate change research, marine ecosystems, and marine engineering.