Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-25T10:18:45.989Z Has data issue: false hasContentIssue false
  • English
  • Français

A Mutual Information-Based Bayesian Network Model for Consequence Estimation of Navigational Accidents in the Yangtze River

Published online by Cambridge University Press:  19 November 2019

Bing Wu ,
Tsz Leung Yip ,
Xinping Yan  and
Zhe Mao
Bing Wu
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hong Kong, China)
Tsz Leung Yip
Affiliation:
(Department of Logistics and Maritime Studies, Hong Kong Polytechnic University, Hong Kong, China)
Xinping Yan
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology, Wuhan, China)
Zhe Mao*
Affiliation:
(Intelligent Transport Systems Research Centre, Wuhan University of Technology, Wuhan, China) (National Engineering Research Center for Water Transport Safety (WTSC), Wuhan University of Technology, Wuhan, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Navigational accidents (collisions and groundings) account for approximately 85% of mari-time accidents, and consequence estimation for such accidents is essential for both emergency resource allocation when such accidents occur and for risk management in the framework of a formal safety assessment. As the traditional Bayesian network requires expert judgement to develop the graphical structure, this paper proposes a mutual information-based Bayesian network method to reduce the requirement for expert judgements. The central premise of the proposed Bayesian network method involves calculating mutual information to obtain the quantitative element among multiple influencing factors. Seven-hundred and ninety-seven historical navigational accident records from 2006 to 2013 were used to validate the methodology. It is anticipated the model will provide a practical and reasonable method for consequence estimation of navigational accidents.

Keywords

Navigational AccidentsConsequence EstimationBayesian NetworkMutual Information
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 3 , May 2020 , pp. 559 - 580
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Akhtar, M. J. and Utne, I. B. (2014). Human fatigue's effect on the risk of maritime groundings–a Bayesian Network modeling approach. Safety Science, 62, 427–440.CrossRefGoogle Scholar
Aven, T. (2010). On how to define, understand and describe risk. Reliability Engineering & System Safety, 95(6), 623–631.CrossRefGoogle Scholar
Balmat, J. F., Lafont, F., Maifret, R. and Pessel, N. (2011). A decision-making system to maritime risk assessment. Ocean Engineering, 38(1), 171–176.Google Scholar
Baniela, S. I., and Ríos, J. V. (2011). Maritime safety standards and the seriousness of shipping accidents. The Journal of Navigation, 64(3), 495–520.CrossRefGoogle Scholar
Erol, S., Demir, M., Çetişli, B. and Eyüboģlu, E. (2018). Analysis of ship accidents in the Istanbul Strait using neuro-fuzzy and genetically optimised fuzzy classifiers. The Journal of Navigation, 71(2), 419–436.CrossRefGoogle Scholar
Goerlandt, F. and Montewka, J. (2015). A framework for risk analysis of maritime transportation systems: a case study for oil spill from tankers in a ship–ship collision. Safety Science, 76, 42–66.CrossRefGoogle Scholar
Gouveia, J. V. and Guedes Soares, C. (2010). Oil spill incidents in Portuguese waters. In: Guedes Soares, C. and Parunov, J. (eds.). Advanced Ship Design for Pollution Prevention. London: Taylor & Francis Group, 217–223.CrossRefGoogle Scholar
Hänninen, M. and Kujala, P. (2012). Influences of variables on ship collision probability in a Bayesian belief network model. Reliability Engineering & System Safety, 102, 27–40.CrossRefGoogle Scholar
Jasionowski, A. (2011). Decision support for ship flooding crisis management. Ocean Engineering, 38(14), 1568–1581.CrossRefGoogle Scholar
Kaplan, S. (1997). The words of risk analysis. Risk Analysis, 17(4), 407–417.Google Scholar
Li, Y. F., Xie, M. and Goh, T. N. (2009). A study of mutual information based feature selection for case based reasoning in software cost estimation. Expert Systems with Applications, 36(3), 5921–5931.CrossRefGoogle Scholar
Li, P., Cai, Q., Lin, W., Chen, B. and Zhang, B. (2016). Offshore oil spill response practices and emerging challenges. Marine Pollution Bulletin, 110(1), 6–27.CrossRefGoogle ScholarPubMed
Mazaheri, A., Montewka, J. and Kujala, P. (2014). Modeling the risk of ship grounding—a literature review from a risk management perspective. WMU Journal of Maritime Affairs, 13(2), 269–297.CrossRefGoogle Scholar
Montewka, J., Weckström, M. and Kujala, P. (2013). A probabilistic model estimating oil spill clean-up costs–a case study for the Gulf of Finland. Marine Pollution Bulletin, 76(1), 61–71.CrossRefGoogle ScholarPubMed
Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K. and Kujala, P. (2014). A framework for risk assessment for maritime transportation systems — a case study for open sea collisions involving RoPax vessels. Reliability Engineering & System Safety, 124, 142–157.CrossRefGoogle Scholar
Nicholson, A. E. and Jitnah, N. (1998). Using mutual information to determine relevance in Bayesian networks. In: Pacific Rim International Conference on Artificial Intelligence. Berlin, Heidelberg: Springer, 399–410.Google Scholar
Peng, H., Long, F. and Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.CrossRefGoogle ScholarPubMed
Pethel, S. D. and Hahs, D. W. (2014). Exact test of independence using mutual information. Entropy, 16(5), 2839–2849.CrossRefGoogle Scholar
Pristrom, S., Yang, Z., Wang, J. and Yan, X. (2016). A novel flexible model for piracy and robbery assessment of merchant ship operations. Reliability Engineering & System Safety, 155, 196–211.CrossRefGoogle Scholar
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press.Google Scholar
Trucco, P., Cagno, E., Ruggeri, F. and Grande, O. (2008). A Bayesian Belief Network modelling of organisational factors in risk analysis: a case study in maritime transportation. Reliability Engineering & System Safety, 93(6), 845–856.CrossRefGoogle Scholar
Wang, J. (2001). The current status and future aspects in formal ship safety assessment. Safety Science, 38(1), 19–30.CrossRefGoogle Scholar
Wang, Y., Zio, E., Wei, X., Zhang, D. and Wu, B. (2019). A resilience perspective on water transport systems: The case of Eastern Star. International Journal of Disaster Risk Reduction, 33, 343–354.CrossRefGoogle Scholar
Weng, J. and Meng, Q. (2011). Analysis of driver casualty risk for different work zone types. Accident Analysis & Prevention, 43(5), 1811–1817.CrossRefGoogle ScholarPubMed
Wróbel, K., Montewka, J. and Kujala, P. (2017). Towards the assessment of potential impact of unmanned vessels on maritime transportation safety. Reliability Engineering & System Safety, 165, 155–169.CrossRefGoogle Scholar
Wu, B., Yan, X., Wang, Y. and Guedes Soares, C. (2017a). An evidential reasoning-based CREAM to human reliability analysis in maritime accident process. Risk Analysis, 37(10), 1936–1957.CrossRefGoogle Scholar
Wu, B., Yan, X., Yip, T.L. and Wang, Y. (2017b). A flexible decision-support solution for intervention measures of grounded ships in the Yangtze River. Ocean Engineering, 141, 237–248.CrossRefGoogle Scholar
Wu, B., Zong, L., Yan, X. and Guedes Soares, C. (2018). Incorporating evidential reasoning and TOPSIS into group decision-making under uncertainty for handling ship without command. Ocean Engineering, 164, 590–603.CrossRefGoogle Scholar
Wu, B., Yip, T. L., Yan, X. and Guedes Soares, C. (2019). Fuzzy logic based approach for ship-bridge collision alert system. Ocean Engineering, 187, 106152.CrossRefGoogle Scholar
Xiong, S., Long, H., Tang, G., Wan, J. and Li, H. (2015). The management in response to marine oil spill from ships in China: A systematic review. Marine Pollution Bulletin, 96(1), 7–17.CrossRefGoogle ScholarPubMed
Yang, Z., Yang, Z. and Yin, J. (2018). Realising advanced risk-based port state control inspection using data-driven Bayesian networks. Transportation Research Part A: Policy and Practice, 110, 38–56.Google Scholar
Yip, T. L., Talley, W. K. and Jin, D. (2011). The effectiveness of double hulls in reducing vessel-accident oil spillage. Marine Pollution Bulletin, 62(11), 2427–2432.CrossRefGoogle ScholarPubMed
Zhao, L., Wang, X. and Qian, Y. (2012). Analysis of factors that influence hazardous material transportation accidents based on Bayesian networks: A case study in China. Safety Science, 50(4), 1049–1055.CrossRefGoogle Scholar
Zhang, D., Yan, X. P., Yang, Z. L., Wall, A. and Wang, J. (2013). Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River. Reliability Engineering & System Safety, 118, 93–105.CrossRefGoogle Scholar
Zhang, J., Yan, X., Zhang, D., Haugen, S. and Yang, X. (2014). Safety management performance assessment for Maritime Safety Administration (MSA) by using generalized belief rule base methodology. Safety Science, 63, 157–167.CrossRefGoogle Scholar
Zhang, J., Teixeira, Â. P., Guedes Soares, C., Yan, X. and Liu, K. (2016a). Maritime transportation risk assessment of Tianjin Port with Bayesian belief networks. Risk Analysis, 36(6), 1171–1187.CrossRefGoogle Scholar
Zhang, D., Yan, X., Zhang, J., Yang, Z. and Wang, J. (2016b). Use of fuzzy rule-based evidential reasoning approach in the navigational risk assessment of inland waterway transportation systems. Safety Science, 82, 352–360.CrossRefGoogle Scholar