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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.02887 |
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| _version_ | 1866917060908417024 |
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| author | Rele, Chaitanya Rathod, Aditya Natu, Kaustubh Kulkarni, Saurabh Koli, Ajay Makdey, Swapnali |
| author_facet | Rele, Chaitanya Rathod, Aditya Natu, Kaustubh Kulkarni, Saurabh Koli, Ajay Makdey, Swapnali |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02887 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets Rele, Chaitanya Rathod, Aditya Natu, Kaustubh Kulkarni, Saurabh Koli, Ajay Makdey, Swapnali Machine Learning Artificial Intelligence 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. |
| title | Predicting Weekly Fishing Concentration Zones through Deep Learning Integration of Heterogeneous Environmental Spatial Datasets |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.02887 |