A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal

Wentao JIA; Xun GONG; Shanliang ZHU; Jifeng QI; Xianmei ZHOU; Hengkai YAO; Xiang GONG; Wenwu WANG

doi:10.1007/s00343-024-4122-9

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A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal

Physics | Updated：2025-08-07

- A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal
- Journal of Oceanology and Limnology Vol. 43, Issue 4, Pages: 1075-1092(2025)
- Affiliations：
  
  1.School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
  2.Institute for Advanced Marine Research, China University of Geosciences, Guangzhou 511462, China
  3.State Key Laboratory of Biogeology and Environmental Geology, Hubei Key Laboratory of Marine Geological Resources, China University of Geosciences, Wuhan 430074, China
  4.Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
  5.Qingdao Innovation Center of Artificial Intelligence Ocean Technology, Qingdao 266061, China
  6.Key Laboratory of Ocean Observation and Forecasting and Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China
  7.University of Chinese Academy of Sciences, Beijing 100049, China
- Author bio：
  
  zhushanliang@qust.edu.cn
  jfqi@qdio.ac.cn
- Funds：
- DOI：10.1007/s00343-024-4122-9
  CLC：
- Received：09 May 2024，
  
  Published：01 July 2025
- Accepted：
Scan QR Code
JIA Wentao,GONG Xun,ZHU Shanliang,et al.A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J].Journal of Oceanology and Limnology,2025,43(04):1075-1092.
DOI：

JIA Wentao,GONG Xun,ZHU Shanliang,et al.A dual-attention embedded CNN model for estimating mixed layer depths in the Bay of Bengal[J].Journal of Oceanology and Limnology,2025,43(04):1075-1092. DOI： 10.1007/s00343-024-4122-9.

摘要

Abstract

Variations in ocean mixed layer depth (MLD) show a significant impact on energy balance in the global climate systems and marine ecosystems. At present

the accuracy of modeling MLD

especially in the region with complex ocean dynamics

remains a challenge

thus calling for an emergency using artificial intelligence approach to improve the assessment of the MLD. A novel convolutional neural network model was developed based on a dual-attention module (DA-CNN) to estimate the MLD in the Bay of Bengal (BoB) by integrating multi-source remote sensing data and Argo gridded data. Compared with the original CNN model

the DA-CNN model exhibits superior performance with notable improvements in the annual average root mean square error (RMSE) and

values by 13.0% and 8.4%

respectively

while more accurately capturing the seasonal variations in MLD. Moreover

the results using the DA-CNN model show minimum RMSE and maximum

values

in comparison to the calculation by the random forest

artificial neural network model

and the hybrid coordinate ocean model. Accordingly

our findings suggest that the newly developed DA-CNN model provides an effective advantage in studying the MLD and the associated ocean processes.

关键词

Keywords

references

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