Application of data-mining techniques to predict and rank maritime non-conformities in tanker shipping companies using accident inspection reports
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.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/17445302
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Supplemental Notes:
- © 2020 Informa UK Limited, trading as Taylor & Francis Group 2020. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Navas de Maya, Beatriz
- Arslan, Ozcan
- Akyuz, Emre
- Kurt, Rafet Emek
- Turan, Osman
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 687-694
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Serial:
- Ships and Offshore Structures
- Volume: 17
- Issue Number: 3
- Publisher: Taylor & Francis
- ISSN: 1744-5302
- Serial URL: http://www.tandfonline.com/tsos20
Subject/Index Terms
- TRT Terms: Data mining; Inspection; Maritime safety; Seaworthiness; Tanker shipping
- Geographic Terms: Turkey
- Subject Areas: Data and Information Technology; Freight Transportation; Marine Transportation; Safety and Human Factors;
Filing Info
- Accession Number: 01847168
- Record Type: Publication
- Files: TRIS
- Created Date: May 25 2022 9:40AM