Identifying Fishing Trip Behavior from Vessel Monitoring System (VMS) Data Using Machine Learning Models
Year: 2024
Type: article
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: Kanda Mahendra, Tanty Oktavia
Institution: Binus University
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402396309