A Dual-Attention Embedded CNN Model for Estimating Mixed Layer Depths in the Bay of Bengal

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 R~ 2 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 R~ 2 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.

Ocean Engineering