Deterministic wave prediction model for irregular long-crested waves with Recurrent Neural Network  

作　　者：Yue Liu Xiantao Zhang Gang Chen Qing Dong Xiaoxian Guo Xinliang Tian Wenyue Lu Tao Peng 

机构地区：[1]State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University(SJTU),Shanghai 200240,China [2]SJTU Yazhou Bay Institute of Deepsea Technology,Sanya,Hainan,572000,China

出　　处：《Journal of Ocean Engineering and Science》2024年第3期251-263,共13页海洋工程与科学（英文）

基　　金：supported by Hainan Provincial Natural Science Foundation of China(Grant no.520QN290);the 2020 Research Pro-gram of Sanya Yazhou Bay Science and Technology City(Grant No.SKJC-2020-01-006);the Hainan Provincial Science and Technol-ogy Plan-Sanya Yazhou Bay Science and Technology City Natural Science Foundation Joint Project(2021JJLH0062).

摘　　要：Real-time predicting of stochastic waves is crucial in marine engineering.In this paper,a deep learning wave prediction(Deep-WP)model based on the‘probabilistic’strategy is designed for the short-term prediction of stochastic waves.The Deep-WP model employs the long short-term memory(LSTM)unit to collect pertinent information from the wave elevation time series.Five irregular long-crested waves generated in the deepwater offshore basin at Shanghai Jiao Tong University are used to validate and optimize the Deep-WP model.When the prediction duration is 1.92s,2.56s,and,3.84s,respectively,the predicted results are almost identical with the ground truth.As the prediction duration is increased to 7.68s or 15.36s,the Deep-WP model’s error increases,but it still maintains a high level of accuracy during the first few seconds.The introduction of covariates will improve the Deep-WP model’s performance,with the absolute position and timestamp being particularly advantageous for wave prediction.Furthermore,the Deep-WP model is applicable to predict waves with different energy components.The proposed Deep-WP model shows a feasible ability to predict nonlinear stochastic waves in real-time.

关 键 词：Real-time wave prediction PROBABILITY LSTM COVARIATE 

分 类 号：P731.22[天文地球—海洋科学]