Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports

Beatriz Navas de Maya, Ozcan Arslan, Emre Akyuz, Rafet Emek Kurt, Osman Turan

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
90 Downloads (Pure)

Abstract

The application of data mining techniques is an extended practice in numerous domains; however, within the context of maritime inspections, the aforementioned methods are rarely applied. Thus, the application of data-mining techniques for the prediction and ranking of non-conformities identified during vessel inspections could be of significant managerial contribution to the safety of shipping companies, as non-conformities could potentially lead to real accidents if not addressed adequately. Hence, specific data mining methods are investigated and applied in this paper to predict and rank non-conformities on oil tankers using a database recorded by tanker shipping companies in Turkey from 2006 to 2019. The results of this study reveal that specific non-conformities, for instance, inadequate ice operations or inadequate general appearance and condition of hull, superstructure and external weather decks, are not company-based problems, rather they are industry wide issues for all tanker shipping companies.
Original languageEnglish
Pages (from-to)687-694
Number of pages8
JournalShips and Offshore Structures
Volume17
Issue number3
Early online date22 Dec 2020
DOIs
Publication statusPublished - 4 Mar 2022

Keywords

  • maritime safety
  • non-conformities
  • maritime inspections
  • data mining techniques
  • WEKA

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