Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets
This work addresses the challenge of locating productive fishing grounds for coastal communities in the North Indian Ocean, though it appears incremental as it applies existing deep learning methods to a specific domain.
The researchers tackled the problem of uncertain fishing ground locations in the North Indian Ocean by developing an AI framework to predict Potential Fishing Zones using oceanographic data, resulting in reduced search time and fuel consumption for fishermen.
The North Indian Ocean, including the Arabian Sea and the Bay of Bengal, represents a vital source of livelihood for coastal communities, yet fishermen often face uncertainty in locating productive fishing grounds. To address this challenge, we present an AI-assisted framework for predicting Potential Fishing Zones (PFZs) using oceanographic parameters such as sea surface temperature and chlorophyll concentration. The approach is designed to enhance the accuracy of PFZ identification and provide region-specific insights for sustainable fishing practices. Preliminary results indicate that the framework can support fishermen by reducing search time, lowering fuel consumption, and promoting efficient resource utilization.