Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets

TL;DR

This paper introduces a deep learning framework that integrates environmental data to accurately predict potential fishing zones in the North Indian Ocean, aiding fishermen in efficient and sustainable fishing practices.

Contribution

It presents a novel AI-assisted approach combining heterogeneous oceanographic datasets for improved prediction of fishing zones.

Findings

Supports fishermen by reducing search time

Lowers fuel consumption

Enhances sustainable fishing practices

Abstract

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.