Learning the Spatiotemporal Evolution Law of Wave Field Based on Convolutional Neural Network

Research on the wave field evolution law is highly significant to the fields of offshore engineering and marine resource development. Numerical simulations have been conducted for high-precision wave field evolution, thus providing short-term wave field prediction. However, its evolution occurs over a long period of time, and its accuracy is difficult to improve. In recent years, the use of machine learning methods to study the evolution of wave field has received increasing attention from researchers. This paper proposes a wave field evolution method based on deep convolutional neural networks. This method can effectively correlate the spatiotemporal characteristics of wave data via convolution operation and directly obtain the offshore forecast results of the Bohai Sea and the Yellow Sea. The attention mechanism, multi-scale path design, and hard example mining training strategy are introduced to suppress the interference caused by Weibull distributed wave field data and improve the accuracy of the proposed wave field evolution. The 72- and 480-h evolution experiment results in the Bohai Sea and the Yellow Sea show that the proposed method in this paper has excellent forecast accuracy and timeliness.