Illegal, unreported and unregulated fishing detection with machine learning

Abstract

Illegal, unreported and unregulated fishing is a worldwide problem that causes local and global economic losses, depletes natural resources, alters our diverse ecosystems and takes an undue toll on fisheries. This study describes a machine learning-based strategy for response generation. Identifying data storage and processes has led to the initial development of a viable IUU fishing detection system that classifies vessels for IUU fishing by combining (1) the likelihood that a vessel is fishing using geospatially referenced signal data, and (2) whether or not it is classified The likelihood of fish activity is scored for ships to be within the area of interest, and (3) classification of whether the vessel is allowed to enter its habitable area of interest. In this paper, certain parts of the system were prototyped, including using logistic regression to develop highly predictive catch or no-fish classification models for longlines and trawlers, and identifying whether a vessel was within an area of interest process. In addition, many fishing vessel registries have been identified, which regulate the rights of specific vessels to fish in regulated areas of interest. The accuracy with which fishing models can predict the probability of fishing when vessels have longline or trawl gear is acceptable, and can predict vessels with seine gear, but additional research and analysis are needed. "In ROI" classification models should be extended to score their likelihood of being in ROI instead of outputting true/false judgments. Using machine learning and data analysis skills, the project aims to make further efforts to predict IUU fishing in order to enable law enforcement and ultimately significantly reduce or prevent IUU fishing.