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Towards a probabilistic model for predicting ship besetting in ice in Arctic waters

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  • Fu, Shanshan
  • Zhang, Di
  • Montewka, Jakub
  • Yan, Xinping
  • Zio, Enrico

Abstract

Recently, the melting of sea ice due to global warming has made it possible for merchant ships to navigate through Arctic Waters. However, Arctic Marine Transportation System remains a very demanding, dynamic and complex system due to challenging hydro-meteorological conditions, poorly charted waters and remoteness of the area resulting in lack of appropriate response capacity in case of emergency. In order to ensure a proper safety level for operations such as ship transit within the area, a risk analysis should be carried out, where the relevant factors pertaining to a given operation are defined and organized in a model. Such a model can assist onshore managers or ships’ crews in planning and conducting an actual sea passage through Arctic waters. However, research in this domain is scarce, mainly due to lack of data. In this paper, we demonstrate the use of a dataset and expert judgment to determine the risk influencing factors and develop a probabilistic model for a ship besetting in ice along the Northeast Passage. For that purpose, we adopt Bayesian belief Networks (BBNs), due to their predominant feature of reasoning under uncertainty and their ability to accommodate data from various sources. The obtained BBN model has been validated showing good agreement with available state-of-the-art models, and providing good understanding of the analyzed phenomena.

Suggested Citation

  • Fu, Shanshan & Zhang, Di & Montewka, Jakub & Yan, Xinping & Zio, Enrico, 2016. "Towards a probabilistic model for predicting ship besetting in ice in Arctic waters," Reliability Engineering and System Safety, Elsevier, vol. 155(C), pages 124-136.
    • Handle: RePEc:eee:reensy:v:155:y:2016:i:c:p:124-136
    DOI: 10.1016/j.ress.2016.06.010
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    Citations

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    Cited by:

    1. Kandel, Rajesh & Baroud, Hiba, 2024. "A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    2. Jiang, Dan & Wu, Bing & Cheng, Zhiyou & Xue, Jie & van Gelder, P.H.A.J.M., 2021. "Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    3. Guo, Yunlong & Jin, Yongxing & Hu, Shenping & Yang, Zaili & Xi, Yongtao & Han, Bing, 2023. "Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    4. Zvyagina, Tatiana & Zvyagin, Petr, 2022. "A model of multi-objective route optimization for a vessel in drifting ice," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    5. 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).
    6. Zhang, Chi & Zhang, Di & Zhang, Mingyang & Lang, Xiao & Mao, Wengang, 2020. "An integrated risk assessment model for safe Arctic navigation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 101-114.
    7. Liu, Yang & Ma, Xiaoxue & Qiao, Weiliang & Ma, Laihao & Han, Bing, 2024. "A novel methodology to model disruption propagation for resilient maritime transportation systems–a case study of the Arctic maritime transportation system," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. Rigot-Müller, Patrick & Cheaitou, Ali & Etienne, Laurent & Faury, Olivier & Fedi, Laurent, 2022. "The role of polarseaworthiness in shipping planning for infrastructure projects in the Arctic: The case of Yamal LNG plant," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 330-353.
    9. Liu, Xing & Fang, Yi-Ping & Zio, Enrico, 2021. "A Hierarchical Resilience Enhancement Framework for Interdependent Critical Infrastructures," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Fu, Shanshan & Yu, Yuerong & Chen, Jihong & Xi, Yongtao & Zhang, Mingyang, 2022. "A framework for quantitative analysis of the causation of grounding accidents in arctic shipping," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    11. Li, Huanhuan & Ren, Xujie & Yang, Zaili, 2023. "Data-driven Bayesian network for risk analysis of global maritime accidents," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    12. Goerlandt, Floris & Islam, Samsul, 2021. "A Bayesian Network risk model for estimating coastal maritime transportation delays following an earthquake in British Columbia," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    13. Dinis, D. & Teixeira, A.P. & Guedes Soares, C., 2020. "Probabilistic approach for characterising the static risk of ships using Bayesian networks," Reliability Engineering and System Safety, Elsevier, vol. 203(C).
    14. Zhuang Li & Shenping Hu & Guoping Gao & Yongtao Xi & Shanshan Fu & Chenyang Yao, 2020. "Risk Reasoning from Factor Correlation of Maritime Traffic under Arctic Sea Ice Status Association with a Bayesian Belief Network," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    15. Xu, Sheng & Kim, Ekaterina & Haugen, Stein & Zhang, Mingyang, 2022. "A Bayesian network risk model for predicting ship besetting in ice during convoy operations along the Northern Sea Route," Reliability Engineering and System Safety, Elsevier, vol. 223(C).

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