CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction

Accurate prediction of sea surface temperature (SST) is practically important to ocean-related fields. However, due to the nonlinear temporal characteristics and complex spatial correlation of SST, fully extracting its spatiotemporal features for dependable short-term SST prediction remains challenging. In this work, we presented a multilayer channel spatial attention convolutional LSTM (CSACL) network to better capture the spatiotemporal information of SST recordings. Using multiscale spatial attention to obtain global-local spatial information and dynamic channel attention for interchannel interaction, the CSA module achieves comprehensive extraction of spatial features in SST data. Moreover, the multilayer long short-term memory (LSTM) structure ensures that the network can capture the temporal dependency of short-term SST very well. Finally, we proposed a novel two-stage adaptive loss function training method to solve the accuracy degradation problem for longer prediction cycles. We have evaluated CSACL on three public datasets, and the results demonstrate its superior performance over other classical methods in short-term SST prediction.