Prediction of the Economic Behavior of Fishery Biotechnology Companies Based on Machine Learning-Based Deep Metacellular Automata
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866917596974022656 |
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| author | Chen, Liguo Hua, Hongyang Luo, Xinyue Xu, Guoli Yan, Xu |
| author_facet | Chen, Liguo Hua, Hongyang Luo, Xinyue Xu, Guoli Yan, Xu |
| contents | Ocean warming significantly affects the fishing industry, with species like Scottish herring and mackerel migrating northwards. Our research, a fusion of artificial intelligence, data science, and operations research, addresses this crisis. Using Long Short Term Memory networks, we forecast sea surface temperatures (SST) and model fish migratory patterns with Enhanced Cellular Automata. A corrective factor within our model adjusts for human impact on SST, guiding diverse mitigation scenarios. We apply operational research to strategize responses, including the modernization of fishing vessels as a less costly alternative to relocation. Our data-driven approach, suggesting fleet modernization, strategic relocation, and product diversification, offers an effective approach to mitigating the threats to the ocean warming phenomenon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13509 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Prediction of the Economic Behavior of Fishery Biotechnology Companies Based on Machine Learning-Based Deep Metacellular Automata Chen, Liguo Hua, Hongyang Luo, Xinyue Xu, Guoli Yan, Xu Applications Ocean warming significantly affects the fishing industry, with species like Scottish herring and mackerel migrating northwards. Our research, a fusion of artificial intelligence, data science, and operations research, addresses this crisis. Using Long Short Term Memory networks, we forecast sea surface temperatures (SST) and model fish migratory patterns with Enhanced Cellular Automata. A corrective factor within our model adjusts for human impact on SST, guiding diverse mitigation scenarios. We apply operational research to strategize responses, including the modernization of fishing vessels as a less costly alternative to relocation. Our data-driven approach, suggesting fleet modernization, strategic relocation, and product diversification, offers an effective approach to mitigating the threats to the ocean warming phenomenon. |
| title | Prediction of the Economic Behavior of Fishery Biotechnology Companies Based on Machine Learning-Based Deep Metacellular Automata |
| topic | Applications |
| url | https://arxiv.org/abs/2402.13509 |