Sea surface temperature prediction using a cubic B-spline interpolation and spatiotemporal attention mechanism
Jingjing Liu, Baogang Jin, Jinkun Yang, Lingyu Xu
Abstract
Sea surface temperature (SST) prediction plays an important role in planning marine operations and forecasting climate. With the rapid development of remote sensing technology, there are plenty of SST data available for scientific research. However, most previous studies ignored the quality of SST dataset and the importance of data at each time step, which limited the performance of prediction. Therefore, in order to fully exploit the features of SST data, we propose a model which combines cubic B-spline interpolation, attention mechanism and Long Short Term Memory network (LSTM), named CBSA-LSTM. In this model, we use cubic B-spline interpolation to enhance input data and make SST trend curve continuous derivative in time dimension, adjust time attention mechanism to let it more suitable for SST prediction, refine spatial attention mechanism to mainly focus on latitude, and then combine them with LSTM to predict daily SST. To our knowledge, it is the first attempt to use cubic B-spline interpolation to solve the problem of data quality for SST prediction. The experiment results indicate that the proposed model significantly improve the prediction performance and is reliable for SST prediction with high performance for wide time range and large spatial scope.