Identifying Fishing Trip Behavior from Vessel Monitoring System (VMS) Data Using Machine Learning Models
Journal Title: International Journal of Current Science Research and Review - Year 2024, Vol 7, Issue 09
Abstract
Illegal fishing in Indonesian waters poses a serious challenge that requires innovative solutions. This research offers an advanced technological approach by applying the Hidden Markov Model (HMM) in Machine Learning to address this issue. Data from the Vessel Monitoring System (VMS) is utilized to efficiently identify fishing vessel activities. By involving a dataset that encompasses various vessel activities, this model can detect suspicious fishing practices in real-time. The research findings demonstrate that this model consistently identifies fishing vessel activities with a high level of accuracy. This study makes a significant contribution to efforts in preventing Illegal, Unreported, and Unregulated (IUU) Fishing and supports marine resource sustainability initiatives.
Authors and Affiliations
Kanda Mahendra, Tanty Oktavia,
Keywords
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- EP ID EP744377
- DOI 10.47191/ijcsrr/V7-i9-14
- Views 50
- Downloads 1
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