Time Series AI Enabled Fisheries Management
Open Access DepositedThis praxis examines the impact of rising ocean temperatures on crab populations and explores the use of machine learning to enhance population management strategies by treating the crab lifecycle as a repeatable, modellable engineering system. As global temperatures continue to rise, marine ecosystems are undergoing significant environmental shifts which affect the habitats, breeding patterns, and survival rates of various cold-water species. These changes pose substantial challenges for traditional fisheries management approaches. This study employs machine learning regression algorithms to analyze the extensive World Ocean Database temperature data sets to demonstrate that predicting Alaskan King Crab populations are possible. The results demonstrate the efficacy of Support Vector Machine algorithms (SVMs) in identifying trends and forecasting future population changes. The training and testing of multiple regression algorithms on past Alaskan King Crab Landings and ocean temperatures provides added insight to the future of the Alaskan fisheries and the methods that may be needed for their improved production. This praxis emphasizes the necessity that governing bodies invest in new technologies to enable more effective management of wildlife resources. By leveraging advanced predictive models, fisheries managers can make data-driven decisions to mitigate the effects of climate change, optimize harvest strategies, and ensure the sustainability of cold-water fish populations. This research underscores the potential of machine learning as a transformative tool in marine resource management, offering a scalable, transferable, and proactive solution to address the challenges posed by a warming ocean.
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