Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction

doi:10.1109/TGRS.2021.3126460

IOCAS-IR

	Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction
	Zhou, Yuan 1; Lu, Chang 1; Chen, Keran 1; Li, Xiaofeng 2
	2022
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	60 页码:11
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
摘要	Sea surface height anomaly (SSHA) is vitally important for climate and marine ecosystems. This article develops a multilayer fusion recurrent neural network (MLFrnn) to achieve an accurate and holistic prediction of the SSHA field, given only as a series of past SSHA observations. The proposed approach learns long-term dependencies within the SSHA time series and spatial correlations among neighboring and remote regions. A new multilayer fusion cell as the building block of the MLFrnn model was designed, which fully fused spatial and temporal features. The daily average satellite altimeter SSHA data in the South China Sea from January 1, 2001, to May 13, 2019, were used to train and test the model. We performed a 21-day ahead SSHA prediction and our MLFrnn model has very high accuracy, with a root mean square error (RMSE) of 0.027 m. Compared with existing deep learning networks, the proposed model was superior both in prediction performance and stability, especially on the wide-scale and long-term predictions.
关键词	Computer architecture Microprocessors Predictive models Mathematical models Sea surface Data models Satellites Deep learning (DL) field prediction satellite remote sensing data sea surface height anomaly (SSHA)
DOI	10.1109/TGRS.2021.3126460
收录类别	SCI
语种	英语
资助项目	Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB42000000] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDA19060101] ; National Natural Science Foundation of China-Shandong Science Foundation[U2006211] ; Key Research and Development Project of Shandong Province[2019JZZY010102] ; Key Deployment Project of Center for Ocean Mega-Science, CAS[COMS2019R02] ; CAS[Y9KY04101L]
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000757891700002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：5[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/337002/178243
专题	中国科学院海洋研究所
通讯作者	Li, Xiaofeng
作者单位	1.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Shandong, Peoples R China
通讯作者单位	中国科学院海洋研究所
推荐引用方式 GB/T 7714	Zhou, Yuan,Lu, Chang,Chen, Keran,et al. Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2022,60:11.
APA	Zhou, Yuan,Lu, Chang,Chen, Keran,&Li, Xiaofeng.(2022).Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,60,11.
MLA	Zhou, Yuan,et al."Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 60(2022):11.

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