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

Navas de Maya, Beatriz and Arslan, Ozcan and Akyuz, Emre and Kurt, Rafet Emek and Turan, Osman (2022) Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports. Ships and Offshore Structures, 17 (3). pp. 687-694. ISSN 1754-212X (https://doi.org/10.1080/17445302.2020.1862530)

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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.

ORCID iDs

Navas de Maya, Beatriz ORCID logoORCID: https://orcid.org/0000-0002-3595-9401, Arslan, Ozcan, Akyuz, Emre, Kurt, Rafet Emek ORCID logoORCID: https://orcid.org/0000-0002-5923-0703 and Turan, Osman;
CORE (COnnecting REpositories)