IOCAS-IR  > 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, Xudong1,2; Li, Xiaofeng1,2; Zheng, Quanan1,2
2021
发表期刊IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
ISSN1939-1404
卷号14页码:3095-3106
通讯作者Li, Xiaofeng(xiaofeng.li@ieee.org)
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.
关键词Predictive models Training Satellites Machine learning Oceans MODIS Data models Andaman sea internal wave (IW) forecast machine learning
DOI10.1109/JSTARS.2021.3063529
收录类别SCI
语种英语
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
WOS类目Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号WOS:000634496000002
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:17[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/337002/170291
专题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, CAS Key Lab Ocean Circulat & Waves, Inst Oceanol, Qingdao 266071, Peoples R China
2.Chinese Acad Sci, Ctr Ocean Megasci, Qingdao 266071, Peoples R China
第一作者单位中国科学院海洋研究所;  中国科学院海洋大科学研究中心
通讯作者单位中国科学院海洋研究所;  中国科学院海洋大科学研究中心
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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.
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