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

Navas de Maya B., Arslan Ö., AKYUZ E., Kurt R. E., Turan O.

Ships and Offshore Structures, vol.17, no.3, pp.687-694, 2022 (SCI-Expanded, Scopus) identifier identifier

  • Nəşrin Növü: Article / Article
  • Cild: 17 Say: 3
  • Nəşr tarixi: 2022
  • Doi nömrəsi: 10.1080/17445302.2020.1862530
  • jurnalın adı: Ships and Offshore Structures
  • Jurnalın baxıldığı indekslər: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Compendex, Computer & Applied Sciences, INSPEC
  • Səhifə sayı: pp.687-694
  • Açar sözlər: data mining techniques, maritime inspections, Maritime safety, non-conformities, WEKA
  • Açıq Arxiv Kolleksiyası: Məqalə
  • Adres: Yox

Qısa məlumat

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.