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Catching Illegal Fishing using Random Forest and Linear Regression Models

Mounika*, Sowjanya**, Keerthana***, Tejaswini****

*-**** Vignan's Institute of Engineering for Women, Andhra Pradesh, India.

Periodicity:April - June'2022
DOI : https://doi.org/10.26634/jse.16.4.18818

Abstract

Globally, illegal fishing is posing a serious financial challenge to the fishing industry. Because of this, illegal fishing pushes many fish populations to extinction. This paper proposes analyzing illegal fishing using data analysis and machine learning techniques. The existing methods use data manipulation in the illegal fishing data due to the delay in catching illegal vessels, while in this system the data is entered manually. This paper offers data analytics to find these vessels. It may collect raw data from Global Fishing Watch (GFW) and analyze the data to find vessels, whether it is used for illegal or legal fishing. Based on the sensors attached to the ship, it can find data about the Automatic Identification System (AIS) location, the ship's type, and the ship's speed. This model can predict illegal fishing and take appropriate action against illegal fishing vessels.

Keywords

Illegal Fishing, Normal Fishing, Regression Model, GFW.

How to Cite this Article?

Mounika, Sowjanya, Keerthana, and Tejaswini. (2022). Catching Illegal Fishing using Random Forest and Linear Regression Models. i-manager’s Journal on Software Engineering, 16(4), 17-23. https://doi.org/10.26634/jse.16.4.18818

References

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Catching Illegal Fishing using Random Forest and Linear Regression Models

Abstract

Keywords

How to Cite this Article?

References

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