Sumangala, D.; Abhinav, G.; Nagamani, P.V., and Warrior, H., 2024. Enhanced upwelling dynamics simulation in the Bay of Bengal: Integrating ANNs into hydrodynamic models. Journal of Coastal Research, 40(6), 1125–1136. Charlotte (North Carolina), ISSN 0749-0208.

The Bay of Bengal exhibits a unique upwelling pattern, which is most pronounced during the summer monsoon months. This phenomenon is driven by a combination of open-ocean processes and seasonally reversing winds, leading to the transport of cooler, nutrient-rich waters to the coastal areas. The upwelling process is crucial for the marine ecosystem, supporting a diverse range of organisms that thrive in colder temperatures. To accurately capture the upwelling dynamics in the Bay of Bengal, a combination of the Delft3D model and an artificial neural network (ANN) was employed. The Delft3D model alone was unable to adequately represent the upwelling phenomenon, particularly the vertical velocity component (w). The incorporation of an ANN into the Delft3D framework significantly improved the accuracy of vertical velocity predictions. This modification provided a better capacity to forecast velocities in the Bay of Bengal, with a correlation of up to 0.92 (0.97) for the u (v) velocity components. The findings of this study highlight the importance of incorporating ANNs into hydrodynamic models to accurately represent complex processes such as upwelling. This approach provides a more comprehensive understanding of these critical processes, enabling better management and conservation of marine ecosystems.