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

Chaitanya Rele, Aditya Rathod, Kaustubh Natu, Saurabh Kulkarni, Ajay Koli +1 more
11/4/2025
cs.LGcs.AI

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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.

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Cite this paper

@article{rele2025predicting,
  title  = {Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets},
  author = {Chaitanya Rele and Aditya Rathod and Kaustubh Natu and Saurabh Kulkarni and Ajay Koli and Swapnali Makdey},
  year   = {2025},
  eprint = {2511.02887},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.02887v1}
}

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