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Journal of Homeland Security and Emergency Management

Editor-in-Chief: Renda-Tanali, Irmak

Managing Editor: McGee, Sibel

4 Issues per year

IMPACT FACTOR 2017: 0.712

CiteScore 2017: 0.92

SCImago Journal Rank (SJR) 2017: 0.242
Source Normalized Impact per Paper (SNIP) 2017: 0.615

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Behavior Analysis of Illegal Fishing in the Gulf of Mexico

Ali Pala
  • Turkish Military Academy, Ankara, Turkey
  • Department of Industrial and Systems Engineering, University at Buffalo, Buffalo, NY, USA
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jing Zhang / Jun Zhuang / Nathan Allen
Published Online: 2018-02-17 | DOI: https://doi.org/10.1515/jhsem-2016-0017


Illegal fishing activities in the Gulf of Mexico pose a threat to the US national security, as well as damage to the economy. The US Coast Guard (USCG) estimates over 1100 incursions by Mexican fisherman into US regulated waters in the Gulf of Mexico annually. Fishermen enter the water borders to catch red snapper, which is one of the Gulf of Mexico’s signature and most valuable fish. There are a number of academic contributions which have sought to improve the understanding of the problem of illegal fishing, and to try to generate better solutions. In this study, we investigate the relationship between illegal fishing activities and environmental factors with one-year of historical sight, weather, and moon phase data. Descriptive analysis provides some interesting insights such as sight patterns depending on wave height, moon phase, and hours of a day. Also, we develop logistic regression models that shows wave height is negatively correlated with sight occurrences for all sight types. In addition, we oversample the data and develop two pre diction models using logistic regression and k-nearest neighbor algorithm and compare prediction accuracies. The results show that k-nearest neighbor algorithm performs better in most of the cases.

Keywords: border security; illegal fishing; regression analysis; statistical inference


About the article

Published Online: 2018-02-17

Funding Source: Division of Civil, Mechanical and Manufacturing Innovation

Award identifier / Grant number: 1200899

Division of Civil, Mechanical and Manufacturing Innovation, Funder Id: 10.13039/100000147, Grant Number: 1200899.

Citation Information: Journal of Homeland Security and Emergency Management, Volume 15, Issue 1, 20160017, ISSN (Online) 1547-7355, DOI: https://doi.org/10.1515/jhsem-2016-0017.

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