Study on the Influence of Attention Mechanism in Large-Scale Sea Surface Temperature Prediction Based on Temporal Convolutional Network

Abstract

The short term and small-scale sea surface temperature prediction using deep learning has achieved good results. The long-term sea surface temperature prediction technology in large-scale sea area is limited by the large and complex data. So how to use deep learning to select more valuable data and realize high precision of sea surface temperature prediction is an important problem. In this paper, attention mechanism and Temporal convolutional network (TCN) are used to predict the Indian Ocean 40°E–110°E and –25°S–25°N from 2015 to 2018 with 1° × 1° spatial resolution. The attention mechanism is used to distinguish the importance of the data, and the prediction models of full-feature (81 dimensions) and partial-feature (66 dimensions) are constructed. The experimental results show that the fitting degree of partial-feature models to sea surface temperature time series does not decrease significantly. The method proposed in this paper uses less data to ensure that the experimental accuracy does not decline significantly, and improves the long-term sea surface temperature prediction technology in large-scale sea area.