A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea

doi:10.1109/JSTARS.2021.3063529

CORC > 海洋研究所 > 中国科学院海洋研究所 > 海洋环流与波动重点实验室

	A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea
	Zhang, Xudong 1,2; Li, Xiaofeng 1,2; Zheng, Quanan 1,2
刊名	IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
	2021
卷号	14 页码:3095-3106
关键词	Predictive models Training Satellites Machine learning Oceans MODIS Data models Andaman sea internal wave (IW) forecast machine learning
ISSN号	1939-1404
DOI	10.1109/JSTARS.2021.3063529
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要	Internal waves (IWs) are broadly distributed globally and have significant impacts on offshore engineering and underwater navigation. The prediction of IW propagation is a challenging task because of the complex factors involved. In this study, a machine-learning model was developed to predict IW propagation in the Andaman Sea. The model is based on a back-propagation neural network trained by 1189 IW samples, including the crest length and the peak-to-peak distance of IWs, extracted from 123 Moderate-Resolution Imaging Spectroradiometer (MODIS) images and 33 Ocean Land Color Instrument (OLCI) images acquired from 2015 to 2019 and corresponding ocean environment parameters. Using the leading wave crest within an IW packet as input, we ran the model to forecast the IW locations and compare them with satellite observations. The average root-mean-square difference between the model-forecasted and satellite-observed IW leading crest after one tidal cycle was 3.21 km. The corresponding averaged correlation coefficient was 0.95 and the average Frechet Distance was 11.46 km. We reiterated the model run over two tidal periods and obtained similar statistical results, indicating the robustness of forecasting IW packets. We find that reducing the time step helped to improve forecasting accuracy. The influence of input errors and seasonal variations on model results are discussed and an analysis shows that the initial propagation direction introduced to the model is necessary for cross-propagating IW patterns. Comparisons with the Korteweg-de Vries equation results show that the developed model has better performance and is more robust.
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19090103] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000] ; National Natural Science Foundation for Young Scientists of China[41906157] ; National Natural Science Foundation for Young Scientists of China[41604200] ; National Natural Science Foundation of China[41776183] ; Major Scientific, and Technological Innovation Projects in Shandong Province[2019JZZY010102] ; Key Project of Center for Ocean Mega-Science, Chinese Academy of Sciences[COMS2019R02] ; CAS Program[Y9KY04101 L]
WOS研究方向	Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000634496000002
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/170290]
专题	海洋研究所_海洋环流与波动重点实验室
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, CAS Key Lab Ocean Circulat & Waves, Inst Oceanol, Qingdao 266071, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Xudong,Li, Xiaofeng,Zheng, Quanan. A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:3095-3106.
APA	Zhang, Xudong,Li, Xiaofeng,&Zheng, Quanan.(2021).A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,3095-3106.
MLA	Zhang, Xudong,et al."A Machine-Learning Model for Forecasting Internal Wave Propagation in the Andaman Sea".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):3095-3106.