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

doi:10.1109/TGRS.2021.3126460

CORC > 海洋研究所 > 中国科学院海洋研究所

	Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction
	Zhou, Yuan 2; Lu, Chang 2; Chen, Keran 2; Li, Xiaofeng 1
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2022
卷号	60 页码:11
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)
ISSN号	0196-2892
DOI	10.1109/TGRS.2021.3126460
通讯作者	Li, Xiaofeng(xiaofeng.li@ieee.org)
英文摘要	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.
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
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000757891700002
内容类型	期刊论文
源URL	[http://ir.qdio.ac.cn/handle/337002/178242]
专题	中国科学院海洋研究所
通讯作者	Li, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Oceanol, Qingdao 266071, Shandong, Peoples R China 2.Tianjin Univ, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China
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.