A forecasting model for wave heights based on a long short-term memory neural network

作     者：Song Gao Juan Huang Yaru Li Guiyan Liu Fan Bi Zhipeng Bai Song Gao;Juan Huang;Yaru Li;Guiyan Liu;Fan Bi;Zhipeng Bai

作者机构：North China Sea Marine Forecasting Center of State Oceanic AdministrationQingdao 266061China Shandong Provincial Key Laboratory of Marine Ecological Environment and Disaster Prevention and MitigationQingdao 266061China Mailbox 5111Beijing 100094China 

出 版 物：《Acta Oceanologica Sinica》 (海洋学报（英文版）)

年 卷 期：2021年第40卷第1期

页      面：62-69页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 0707[理学-海洋科学] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The National Key R&D Program of China under contract No.2016YFC1402103 

主　　题：long short-term memory marine forecast neural network significant wave height 

摘      要：To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been *** model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training *** prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),***,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction ***,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys *** experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for ***,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.