Identifying and Combating Unlawful Fishing Activities: A Classification-Based Approach

  • Maanaav Motiramani
  • Parth Mody
  • Param Sejpal


Illegal, unreported, and unregulated (IUU) fishing has become a very profitable business, with nearly $23.5 billion worth of fish stolen from the world’s oceans. Every year fishing business grows efficiently, and Illegal fishing is one of the big back steps in this business. Furthermore, many fish populations are pushed to extinction. This paper proposes illegal fishing detection using data analytics and machine learning techniques. The primary data was gathered from the Global Fishing Watch (GFW) and the data was analyzed to find whether the vessels are used for illegal or legal fishing based on the sensors attached to the vessel GFW found location data, type of the vessel, and speed of the vessel. Even with the available data, trying to solve this problem globally is a very difficult task. By these classification models (Logistic Regression, SVM, KNN, Naïve Bayes, XG Boost, Decision Tree, Random Forest Classifier), we can predict illegal fishing and can take necessary actions against the illegal fishing boats.

Keywords: Illegal, unregulated, and unlicensed fishing detection, Machine learning model, Classification, Global Fishing Watch


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How to Cite
Motiramani, M., Mody, P., & Sejpal, P. (2023). Identifying and Combating Unlawful Fishing Activities: A Classification-Based Approach. Asian Journal For Convergence In Technology (AJCT) ISSN -2350-1146, 9(1), 49-54.