Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method

doi:10.3390/jmse10060836

IOCAS-IR > 海洋环流与波动重点实验室

	Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method
	Feng, Zhijie 1; Hu, Po2,3,4 ; Li, Shuiqing 2,3,4; Mo, Dongxue 2,3
	2022-06-01
发表期刊	JOURNAL OF MARINE SCIENCE AND ENGINEERING
卷号	10 期号:6 页码:20
通讯作者	Hu, Po(hupo@qdio.ac.cn)
摘要	Accurate wave prediction can help avoid disasters. In this study, the significant wave height (SWH) prediction performances of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) were compared. The 10 m u-component of wind (U10), 10 m v-component of wind (V10), and SWH of the previous 24 h were used as input parameters to predict the SWHs of the future 1, 3, 6, 12, and 24 h. The SWH prediction model was established at three different sites located in the Bohai Sea, the East China Sea, and the South China Sea, separately. The experimental results show that the performance of LSTM and GRU networks based on the gating mechanism was better than that of traditional RNNs, and the performances of the LSTM and GRU networks were comparable. The EMD method was found to be useful in the improvement of the LSTM network to forecast the significant wave heights of 12 and 24 h.
关键词	wave height recurrent neural network long short-term memory network GRU EMD
DOI	10.3390/jmse10060836
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060202] ; National Natural Science Foundation of China[42076214] ; National Natural Science Foundation of China[42006027] ; National Natural Science Foundation of China[U1806227]
WOS研究方向	Engineering ; Oceanography
WOS类目	Engineering, Marine ; Engineering, Ocean ; Oceanography
WOS记录号	WOS:000816622700001
出版者	MDPI
引用统计	被引频次：19[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/179690
专题	海洋环流与波动重点实验室
通讯作者	Hu, Po
作者单位	1.Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266580, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Nanhai Rd, Qingdao 266071, Peoples R China 3.Pilot Natl Lab Marine Sci & Technol Qingdao, Lab Ocean Dynam & Climate, Wenhai Rd 1, Qingdao 266237, Peoples R China 4.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
通讯作者单位	海洋环流与波动重点实验室
推荐引用方式 GB/T 7714	Feng, Zhijie,Hu, Po,Li, Shuiqing,et al. Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method[J]. JOURNAL OF MARINE SCIENCE AND ENGINEERING,2022,10(6):20.
APA	Feng, Zhijie,Hu, Po,Li, Shuiqing,&Mo, Dongxue.(2022).Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method.JOURNAL OF MARINE SCIENCE AND ENGINEERING,10(6),20.
MLA	Feng, Zhijie,et al."Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method".JOURNAL OF MARINE SCIENCE AND ENGINEERING 10.6(2022):20.