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Pattern investigation of total loss maritime accidents based on association rule mining

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  • Lan, He
  • Ma, Xiaoxue
  • Ma, Laihao
  • Qiao, Weiliang

Abstract

Total loss of a ship is the most serious consequence of maritime accidents, which not only brings huge property losses, but causes serious human casualties and environmental pollution. Therefore, to reduce the occurrence of such accidents, this study investigates the significant patterns involved in total loss accidents using association rule technique. Based on a total of 1554 total loss accident data from 2010 to 2020 in the Information Handling Services (IHS) sea-web database, the Éclat algorithm is employed to extract association rules from total loss accident dataset associated with accident types and accident severity. The present study indicates that ships aged over 20 years is the key indicator of casualties in total loss accidents. Hull/machinery damage and foundered are the major types of accidents that frequently contribute to ship total loss. The results would help stakeholders involved understand the characteristics of total loss maritime accidents and put forward preventive measures to reduce the occurrence of similar accidents.

Suggested Citation

  • Lan, He & Ma, Xiaoxue & Ma, Laihao & Qiao, Weiliang, 2023. "Pattern investigation of total loss maritime accidents based on association rule mining," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:reensy:v:229:y:2023:i:c:s0951832022005105
    DOI: 10.1016/j.ress.2022.108893
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    References listed on IDEAS

    as
    1. Çakır, Erkan & Fışkın, Remzi & Sevgili, Coşkan, 2021. "Investigation of tugboat accidents severity: An application of association rule mining algorithms," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    2. Wang, Likun & Yang, Zaili, 2018. "Bayesian network modelling and analysis of accident severity in waterborne transportation: A case study in China," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 277-289.
    3. Zhaochen Wang & Jingbo Yin, 2020. "Risk assessment of inland waterborne transportation using data mining," Maritime Policy & Management, Taylor & Francis Journals, vol. 47(5), pages 633-648, July.
    4. Martins, Marcelo Ramos & Maturana, Marcos Coelho, 2013. "Application of Bayesian Belief networks to the human reliability analysis of an oil tanker operation focusing on collision accidents," Reliability Engineering and System Safety, Elsevier, vol. 110(C), pages 89-109.
    5. Yildiz, Serdar & Uğurlu, Özkan & Wang, Jin & Loughney, Sean, 2021. "Application of the HFACS-PV approach for identification of human and organizational factors (HOFs) influencing marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Wang, Huanxin & Liu, Zhengjiang & Wang, Xinjian & Graham, Tony & Wang, Jin, 2021. "An analysis of factors affecting the severity of marine accidents," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    7. Jia, Xiaohui & Zhang, Donghui, 2021. "Prediction of maritime logistics service risks applying soft set based association rule: An early warning model," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Fu, Lipeng & Wang, Xueqing & Zhao, Heng & Li, Mengnan, 2022. "Interactions among safety risks in metro deep foundation pit projects: An association rule mining-based modeling framework," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Zhou, Ying & Li, Chenshuang & Ding, Lieyun & Sekula, Przemyslaw & Love, Peter E.D. & Zhou, Cheng, 2019. "Combining association rules mining with complex networks to monitor coupled risks," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 194-208.
    10. Bye, Rolf J. & Aalberg, Asbjørn L., 2018. "Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 174-186.
    11. Zhang, Yang & Sun, Xukai & Chen, Jihong & Cheng, Cheng, 2021. "Spatial patterns and characteristics of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    12. Gil, Mateusz & Kozioł, Paweł & Wróbel, Krzysztof & Montewka, Jakub, 2022. "Know your safety indicator – A determination of merchant vessels Bow Crossing Range based on big data analytics," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    13. Lan, He & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao, 2022. "On the causation of seafarers’ unsafe acts using grounded theory and association rule," Reliability Engineering and System Safety, Elsevier, vol. 223(C).
    14. Qiao, Wanguan & Liu, Quanlong & Li, Xinchun & Luo, Xixi & Wan, YuLong, 2018. "Using data mining techniques to analyze the influencing factor of unsafe behaviors in Chinese underground coal mines," Resources Policy, Elsevier, vol. 59(C), pages 210-216.
    15. Kandemir, Cagatay & Celik, Metin, 2021. "Determining the error producing conditions in marine engineering maintenance and operations through HFACS-MMO," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    16. Carl Macrae, 2009. "Human factors at sea: common patterns of error in groundings and collisions," Maritime Policy & Management, Taylor & Francis Journals, vol. 36(1), pages 21-38, February.
    17. Fan, Shiqi & Blanco-Davis, Eduardo & Yang, Zaili & Zhang, Jinfen & Yan, Xinping, 2020. "Incorporation of human factors into maritime accident analysis using a data-driven Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
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    Cited by:

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    2. Li, Xin & Chen, Chao & Hong, Yi-du & Yang, Fu-qiang, 2023. "Exploring hazardous chemical explosion accidents with association rules and Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 233(C).

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