Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed

doi:10.1016/j.rse.2022.113328

IOCAS-IR > 海洋环流与波动重点实验室

	Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed
	Zhang, Xudong ; Li, Xiaofeng 1
	2022-12-15
发表期刊	REMOTE SENSING OF ENVIRONMENT
ISSN	0034-4257
卷号	283 页码:16
通讯作者	Li, Xiaofeng([email protected])
摘要	Internal solitary waves (ISW) are widely distributed worldwide and significantly affect the ocean environment and offshore activities. ISW propagation speed is important for ISW forecasts and varies largely globally. This study collected 810 quasi-synchronous optical satellite images with clear ISW signatures in 13 global hotspots to build a large ISW dataset. ISW speed was calculated using extracted ISW wave crest locations and the time difference between satellite image pairs. The dataset contains 57,196 samples, including extracted ISW wave crests and corresponding ISW phase speed. We developed an ISW propagation speed (IPS) model based on the dataset using machine learning techniques. The model structure includes clustering and regression algorithms. The model adopts two tailored modifications to incorporate the ISW domain knowledge and solve the ISW sample distribution imbalance problems. Implementation domain knowledge (IDK) includes selecting relevant ocean factors and ISW properties based on oceanography theory and remote sensing imaging mechanisms. The second tailored modification is adopting advanced model architecture (AMA) by introducing the Gaussian clustering algorithm to classify ISW samples into several groups beyond the limitation of space and time. The extreme gradient boosting regression algorithm was applied in each group to build the IPS model. We used 47,425 samples as the training dataset and the remaining 9771 samples as the test dataset. The model-predicted ISW speed shows good accuracy, with a root mean square error/relative error rate (RER) of 0.16 (7.9) and 0.30 m/s (12.7%) on the training and test dataset. Analysis shows that IDK and AMA improve the model performance by 19.4% and 13.1%, respectively. With a one-pixel error in the peak-to-peak distance of input parameters, the model results degraded from 0.30 m/s to 0.33 m/s. The IPS model was applied to estimate ISW speeds in ocean regions besides the 13 hotspots, and the average RER is 6.0%. ISW forecast in seven ocean areas was tested, and the results indicate that the IPS model can describe ISW propagation patterns. The model results reveal that the ISW phase speed strongly correlates with the spring and neap tide. The IPS model results show that ISW speed is decreased with a deepening stratification. Model-predicted global ISW propagation speed comparison shows that the Celebes Sea and North-West of South America has the fastest and slowest propagating ISWs all year around, respectively. Discussion on the background current's influence on the IPS model results is presented.
关键词	Internal solitary wave Phase speed Machine learning Remote sensing
DOI	10.1016/j.rse.2022.113328
收录类别	SCI
语种	英语
资助项目	Qingdao National Laboratory for Marine Science and Technology; special fund of Shandong province[2022QNLM050301-2]; National Natural Science Foundation for Young Scientists of China[41906157]; National Natural Science Foundation of China[U2006211]; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060101]; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB42000000]
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000878624400001
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：22[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/337002/180462
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, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, 7 Nanhai Rd, Qingdao 266071, Peoples R China 2.Chinese Acad Sci, Ctr Ocean Mega Sci, 7 Nanhai Rd, Qingdao 266071, 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	Zhang, Xudong,Li, Xiaofeng. Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed[J]. REMOTE SENSING OF ENVIRONMENT,2022,283:16.
APA	Zhang, Xudong,&Li, Xiaofeng.(2022).Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed.REMOTE SENSING OF ENVIRONMENT,283,16.
MLA	Zhang, Xudong,et al."Satellite data-driven and knowledge-informed machine learning model for estimating global internal solitary wave speed".REMOTE SENSING OF ENVIRONMENT 283(2022):16.

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