Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model

doi:10.1016/j.dsr.2023.104179

IOCAS-IR > 海洋环流与波动重点实验室

	Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model
	Zhao, Yizhi 1,3,4; Qi, Jifeng 2; Zhu, Shanliang 1,3,4; Jia, Wentao 1,3,4; Gong, Xiang 1,3,4; Yin, Wenming 1,3,4; Yin, Baoshu 2
	2023-12-01
发表期刊	DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS
ISSN	0967-0637
卷号	202 页码:14
通讯作者	Zhu, Shanliang(zhushanliang@qust.edu.cn)
摘要	Accurately and effectively estimating of the barrier layer thickness (BLT) is essential for the research of ocean thermodynamics, ocean dynamics, and air-sea interaction. Artificial intelligence model provides an effective means for accurately estimating BLT from sea surface and gridded Argo data. The present study focuses on the application of a hybrid particle swarm optimization-based artificial neural networks model (PSO-ANN) for estimating the BLT in the Indian Ocean. The input variables of the hybrid model include sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH) and precipitation (P), and the output variable is the BLT value. Multivariate satellite and gridded Argo data collected from the Indian Ocean between January 2012 and December 2019 (i.e., a database consists 239,568 training datasets and 34,224 testing datasets) are provided to prepare the training and testing datasets for the model. The parameters of ANN model, such as network parameter, network weights, and dropout rates are optimized using the PSO algorithm to achieve the best estimation model. R-squared (R2) and root mean square error (RMSE) are used to evaluate the performance of the model. Two groups of comparative experiments (Case 1 and Case 2) on the performance of the PSO-ANN model demonstrate that the model in Case 2 can better capture the complex features of the BLT in the ocean region. The performance of the PSO-ANN model in Case 2 is further compared with the data-driven estimation models such as the traditional ANN model and the known multilinear regression model (MRM), as well as the CESM2-WACCM dynamic model from CMIP6. The comparison results show that the dynamic model has the worst performance among the four models. Moreover, the annual average RMSE value for the PSO-ANN model is 1.83 m, which is 12% and 84% lower than that of the traditional ANN and MRM, respectively. The R2 value for the model of 0.85 is improved by 4% and 40% compared to that of two models. Furthermore, three regions with significant sea-sonal fluctuations of the BLT in the Indian Ocean are selected to further evaluate the estimation accuracy of the hybrid model in 2019; the Southeast Arabian Sea (SEAS), the Bay of Bengal (BoB), and the Eastern Equatorial Indian Ocean (EEIO). As a result, the hybrid model is capable of reflecting seasonal variation trends in these regions, but there is room for improvement in the estimation accuracy. In addition, the correlation analysis between BLT and sea surface parameters indicates that there exist significant correlations between the BLT and SSS, P. The results of this study show that the proposed hybrid model can be used to estimate and analyze BLT in certain regions with complex ocean dynamics processes. Moreover, the model can be extended to estimate other key ocean parameters and provide effective technical support for studying the internal ocean parameters.
关键词	Barrier layer thickness Particle swarm optimization Artificial neural networks Hybrid models
DOI	10.1016/j.dsr.2023.104179
收录类别	SCI
语种	英语
资助项目	Youth Tutor Visiting Study and Training Project of Shandong Province
WOS研究方向	Oceanography
WOS类目	Oceanography
WOS记录号	WOS:001101707700001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS关键词	INTERANNUAL SALINITY VARIABILITY ; SEA-SURFACE SALINITY ; MIXED-LAYER ; EQUATORIAL PACIFIC ; IN-SITU ; TOGA DECADE ; TEMPERATURE ; PREDICTION ; ALGORITHM ; BENGAL
引用统计
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/183958
专题	海洋环流与波动重点实验室
通讯作者	Zhu, Shanliang
作者单位	1.Qingdao Univ Sci & Technol, Sch Math & Phys, Qingdao, Peoples R China 2.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao, Peoples R China 3.Qingdao Innovat Ctr Artificial Intelligence Ocean, Qingdao, Peoples R China 4.Qingdao Univ Sci & Technol, Res Inst Math & Interdisciplinary Sci, Qingdao, Peoples R China
推荐引用方式 GB/T 7714	Zhao, Yizhi,Qi, Jifeng,Zhu, Shanliang,et al. Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model[J]. DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS,2023,202:14.
APA	Zhao, Yizhi.,Qi, Jifeng.,Zhu, Shanliang.,Jia, Wentao.,Gong, Xiang.,...&Yin, Baoshu.(2023).Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model.DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS,202,14.
MLA	Zhao, Yizhi,et al."Estimation of the barrier layer thickness in the Indian Ocean based on hybrid neural network model".DEEP-SEA RESEARCH PART I-OCEANOGRAPHIC RESEARCH PAPERS 202(2023):14.

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