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

作　　者：LIU Xing GAO Zhiyi HOU Fang SUN Jinggao 

机构地区：[1]School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China [2]National Marine Environmental Forecasting Center,Beijing 100081,China

出　　处：《Journal of Ocean University of China》2022年第5期1109-1117,共9页中国海洋大学学报（英文版）

基　　金：supported by the National Key Research and Development Project(No.2018YFC1407001).

摘　　要：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 spa-tiotemporal 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 evolu-tion.The 72-and 480-h evolution experiment results in the Bohai Sea and the Yellow Sea show that the proposed method in this pa-per has excellent forecast accuracy and timeliness.

关 键 词：wave evolution machine learning convolutional neural network hard example mining 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] P731.22[自动化与计算机技术—控制科学与工程]