Ocean Current Prediction Using the Weighted Pure Attention Mechanism

Ocean current (OC) prediction plays an important role for carrying out ocean-related activities. There are plenty of studies for OC prediction with deep learning to pursue better prediction performance, and the attention mechanism was widely used for these studies. However, the attention mechanism was usually combined with deep learning models rather than purely used to predict OC, or, if it was purely used, did not further optimize the attention weight. Therefore, a deep learning model based on weighted pure attention mechanism is proposed in this paper. This model uses the pure attention mechanism, introduces a weight parameter for the generated attention weight, and moves more attentions from other elements to the key elements based on weight parameter setting. To our knowledge, it is the first attempt to use the weighted pure attention mechanism to improve the OC prediction performance, and it is an innovation for OC prediction. The experiment results indicate that the proposed model can fully take advantage of the strengths from the pure attention mechanism; it can further optimize the pure attention mechanism and significantly improve the prediction performance, and is reliable for OC prediction with high performance for a wide time range and large spatial scope.

Automated summary

A deep learning model based on a weighted pure attention mechanism is proposed in this paper to improve the prediction performance of ocean currents. The experiment results indicate that the model can fully utilize the advantages of the pure attention mechanism, optimize it further, and significantly improve the prediction performance, making it reliable for ocean current prediction in a wide time range and large spatial scope.