Published Paper Details:

Illegal fishing is a pervasive and destructive global issue that poses a significant threat to maritime ecosystems and the resilience of fisheries. Illegal, unregulated, and unreported (IUU) fishing leads to extinction of fishing population. Also, fishing businesses facing a major financial problem throughout the globe due to IUU. The effective identification and monitoring of illegal fishing activities are crucial in order to protect maritime resources and the enforcement of international regulations. This review paper explores the use of geographical area modeling as a promising approach to combat illegal fishing in oceans. We discuss various techniques and technologies that have been employed to detect IUU fishing within specific geographical areas, providing insights into their strengths, weaknesses, and future prospects. We will develop machine learning algorithms to analyze the geospatial data and identify patterns associated with illegal fishing activities. The current study attempted to conduct a methodical and critical analysis of around Sixty (60) prior publications that were pertinent and published in academic journals throughout a twenty two-year period (2000 - 2022) in modelling of geographical areas to detect illegal fishing. Specifically :(i) Anomaly detection to identify irregular fishing behaviors, such as fishing in prohibited areas(ii) The application of machine learning techniques and how well they match this domain. (iii) the ideas and elements influencing modelling of geographical areas and the starting point of study, (iv) the pertinent mistake and accuracy metrics. Finally, we summarize the difficulties and prospects for more study in the identification of illicit