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Applying intelligent methods in detecting maritime smuggling

Author

Listed:
  • Chih-Hao Wen

    (Department of Logistics Management, National Defense University)

  • Ping-Yu Hsu

    (Department of Business Administration, National Central University)

  • Ming-Shien Cheng

    (Department of Business Administration, National Central University
    Department of Industrial Engineering and Management, Ming Chi University of Technology)

Abstract

Taiwan is an island state characterized by well-developed maritime traffic and sea transportation. Smuggling by sea increasingly threatens the country’s social and internal security. The Coast Guard Administration is the main actor intercepting smuggling. This study applies classification trees and Bayesian network algorithms to create dependent and independent classification tree models and Bayesian network models for improving the smuggling vessel recognition rates. We focus on relationships of dependence among smuggled goods (for example, fishery goods, firearms and drugs) and variables (such as information on the home port, the age of boat owners, as well as the arrival and departure dates of vessels). The paper presents a selection method for vessels which could be applicable in a number of other maritime instances, such as, for instance, Port State Control inspection. Our findings help in improving recognition accuracy and reduce the potential harm to social and national security by facilitating the identification of vessels with the largest smuggling value.

Suggested Citation

  • Chih-Hao Wen & Ping-Yu Hsu & Ming-Shien Cheng, 2017. "Applying intelligent methods in detecting maritime smuggling," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(3), pages 573-599, August.
    • Handle: RePEc:pal:marecl:v:19:y:2017:i:3:d:10.1057_mel.2016.3
    DOI: 10.1057/mel.2016.3
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    References listed on IDEAS

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    Cited by:

    1. Xueni Gou & Jasmine Siu Lee Lam, 2019. "Risk analysis of marine cargoes and major port disruptions," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 21(4), pages 497-523, December.

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