부산대학교 도서관

Predicting the 3-D ocean temperature field is a significant task that helps to understand global climate change and the state of ocean motion. Lots of numerical and data-driven models are used to predict ocean temperatures. However, these methods are restricted to the time-sequence prediction of discrete points or rely on convolutional layers to inefficiently capture local spatial dependencies for spatio-temporal prediction. In this letter, we propose a deep learning model named self-attention predictive recurrent neural network (SA-PredRNN) that combines attention mechanisms and predictive recurrent neural networks to capture global positional correlations and spatio-temporal features. Global gridded Argo temperature data with Barnes objective analysis (BOA-ARGO) are used to predict the future 3-D ocean temperature. The average root mean square errors (RMSEs) of the proposed model are promoted by at most 11% and 10%, which indicates that the SA-PredRNN model has a better performance than the other baseline models.