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Analysis of fishing vessel accidents with Bayesian network and Chi-square methods

Ugurlu, F, Yildiz, S, Boran, M, Ugurlu, O and Wang, J (2020) Analysis of fishing vessel accidents with Bayesian network and Chi-square methods. Ocean Engineering, 198. ISSN 0029-8018

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Abstract

Commercial fishing is an important industry that generates income directly or indirectly to many people in the world. It is impossible to carry out a fishing activity on this scale without a vessel. Therefore, fishing vessels are the most important element of modern fishing industry. Fishing vessels play a key role in fishing, transporting and storing fish. Thousands of people die every year as a result of fishing vessel accidents. In order to carry out sustainable fishing operations, fishing vessel accidents should be investigated and measures should be taken to prevent them. Therefore, in this study for analysing of accidents occurred between 2008 and 2018 in fishing vessels, with full lengths of 7 m and above, Bayesian network, chi-square methods were used. As a result, recommendations were made to prevent accidents. Also, Accident (Bayes) Network, which summarizes the occurrence of accidents on fishing vessels, is presented. These networks allow to understand the occurrence of accidents in fishing vessels and to estimate the occurrence of accidents in variable conditions. It was also found that there was a significant relationship between accident category and vessel length, vessel age, loss of life and loss of vessel.

Item Type: Article
Uncontrolled Keywords: 0905 Civil Engineering, 0911 Maritime Engineering, 0405 Oceanography
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
V Naval Science > V Naval Science (General)
V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions: Maritime & Mechanical Engineering
Publisher: Elsevier
Related URLs:
Date Deposited: 22 Apr 2020 10:53
Last Modified: 22 Apr 2020 11:00
DOI or Identification number: 10.1016/j.oceaneng.2020.106956
URI: http://researchonline.ljmu.ac.uk/id/eprint/12787

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