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Towards a probabilistic model for estimation of grounding accidents in fluctuating backwater zone of the Three Gorges Reservoir

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  • Jiang, Dan
  • Wu, Bing
  • Cheng, Zhiyou
  • Xue, Jie
  • van Gelder, P.H.A.J.M.

Abstract

The water level fluctuations in the upper reach of dam reservoirs may result in increasing occurrence probabilities of ship grounding accidents, which is especially prominent in the Three Gorges Reservoir. Grounding accidents may not only cause congestion of the traffic but also lead to loss of properties and pollution to the maritime environment. An analytical model incorporating Bayesian Network is proposed to estimate the occurrence likelihood of a ship being grounded in the fluctuating backwater zone. The proposed model comprehensively considers the characteristics of the ship's properties, organizational factors, hydrological conditions and human factors from a systematic perspective. Historical data collected from the Chongqing Maritime Safety Administration, together with incident reports, are used to develop a quantitative model. The developed model in this paper concludes that out of twenty-six factors, the area of the fluctuating backwater zone, the month, and water level are the predominant factors for the occurrence of grounding accidents in the Three Gorges Reservoir. The results can be used by maritime stakeholders to take mitigation measures for grounding accident reduction.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:205:y:2021:i:c:s0951832020307390
    DOI: 10.1016/j.ress.2020.107239
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    Cited by:

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    6. Wu, Bing & Tang, Yuheng & Yan, Xinping & Guedes Soares, Carlos, 2021. "Bayesian Network modelling for safety management of electric vehicles transported in RoPax ships," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    7. Zhang, Mingyang & Montewka, Jakub & Manderbacka, Teemu & Kujala, Pentti & Hirdaris, Spyros, 2021. "A Big Data Analytics Method for the Evaluation of Ship - Ship Collision Risk reflecting Hydrometeorological Conditions," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
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    9. Fan, Lixian & Zhang, Meng & Yin, Jingbo & Zhang, Jinfen, 2022. "Impacts of dynamic inspection records on port state control efficiency using Bayesian network analysis," Reliability Engineering and System Safety, Elsevier, vol. 228(C).

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