Skip to main content
SpringerLink
Log in

A Bayesian Network Approach to Assessing the Risk and Reliability of Maritime Transport

  • Conference paper
  • First Online:
Business Information Systems Workshops (BIS 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 263))

Included in the following conference series:

  • 1567 Accesses

Abstract

The paper presents a conception of a doctoral dissertation, which concerns the problem of estimation of maritime risk and reliability of maritime transport services. In the dissertation a method for dynamic risk assessment based on the Bayesian Network approach is presented. The method concern the risk of an individual ship and its aim is to identify ships, which pose a potential threat due to their individual behaviour and characteristics. Within the article, the following aspects of the dissertation are presented: motivation standing behind the research, main assumptions for the proposed method, its novelty in comparison to existing solutions as well as preliminary results.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Since the research focuses only on the first two steps of FSA, only methods for risk identification and assessment are presented.

  2. 2.

    https://gisis.imo.org.

  3. 3.

    http://www.tokyo-mou.org/.

  4. 4.

    http://www.iomou.org/.

  5. 5.

    http://www.medmou.org/.

  6. 6.

    http://www.bsmou.org/.

  7. 7.

    http://cgmix.uscg.mil/PSIX/PSIXSearch.aspx.

  8. 8.

    https://www.marinetraffic.com.

  9. 9.

    http://maritime-connector.com.

  10. 10.

    http://www.itu.int/en/ITU-R/terrestrial/mars/Pages/MARS.aspx.

  11. 11.

    http://www.iacs.org.uk/shipdata.

  12. 12.

    Correlation analysis was limited to quantitative variables only (age and number of ships flying a given flag). It results from the assumptions to use the following coefficients: Pearson, Kendall and Spearman. Besides, the existence of correlation between ship’s size and accident rate was confirmed using the nCochran-Mantek-Haenschel and Fisher tests.

  13. 13.

    The distribution of original quantitative variables (age and size) is not linear what may influence the results of model’s calculations.

References

  1. Krzysztof, F., Sokołowski, W.: Środki transportu morskiego w zapewnieniu bezpieczeństwa dostaw gazu ziemnego. Logistyka 401 (2012)

    Google Scholar 

  2. el Pozo, F., Dymock, A., Feldt, L., Hebrard, P., di Monteforte, F.S.: Maritime surveillance in support of CSDP. Technical report, European Defence Agency (2010)

    Google Scholar 

  3. Urbański, J., Morgaś, W., Specht, C.: Bezpieczeństwo morskie – ocena i kontrola ryzyka. Zeszyty Naukowe Akademii Marynarki Wojennej 2(173), 53–68 (2008)

    Google Scholar 

  4. Goerlandt, F., Montewka, J.: Maritime transportation risk analysis: review and analysis in light of some foundational issues. Reliab. Eng. Syst. Saf. 138, 115–134 (2015)

    Article  Google Scholar 

  5. Trucco, P., Cagno, E., Ruggeri, F., Grande, O.: A Bayesian belief network modelling of organisational factors in risk analysis: a case study in maritime transportation. Reliab. Eng. Syst. Saf. 93(6), 823–834 (2008)

    Article  Google Scholar 

  6. Berle, O.Y., Asbjørnslett, B.R.E., Rice, J.B.: Formal vulnerability assessment of a maritime transportation system. Reliab. Eng. Syst. Saf. 96(6), 696–705 (2011)

    Article  Google Scholar 

  7. Ellis, J., Forsman, B., Gehl, S., Langbecker, U., Riedel, K., Sames, P.C.: A risk model for the operation of container vessels. WMU J. Marit. Aff. 7(1), 133–149 (2008)

    Article  Google Scholar 

  8. ABS: Guidance notes on Risk Assessement Applications for the Marine and Offshore Oil and Gas Industries. Technical report, American Bureau of Shipping (2000)

    Google Scholar 

  9. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)

    Article  Google Scholar 

  10. Laxhammar, R., Falkman, G.: Conformal prediction for distribution-independent anomaly detection in streaming vessel data. In: Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques, pp. 47–55. ACM (2010)

    Google Scholar 

  11. Lee, C.J., Lee, K.J.: Application of bayesian network to the probabilistic risk assessment of nuclear waste disposal. Reliab. Eng. Syst. Saf. 91(5), 515–532 (2006)

    Article  Google Scholar 

  12. Soares, C.G., Teixeira, A.P.: Risk assessment in maritime transportation. Reliab. Eng. Syst. Saf. 74(3), 299–309 (2001)

    Article  Google Scholar 

  13. Balmat, J.F., Lafont, F., Maifret, R., Pessel, N.: MAritime RISk Assessment (MARISA), a fuzzy approach to define an individual ship risk factor. Ocean Eng. 36(15–16), 1278–1286 (2009)

    Article  Google Scholar 

  14. Elsayed, T.: Fuzzy inference system for the risk assessment of liquefied natural gas carriers during loading/offloading at terminals. Appl. Ocean Res. 31(3), 179–185 (2009)

    Article  Google Scholar 

  15. Mascaro, S., Nicholson, A.E., Korb, K.B.: Anomaly detection in vessel tracks using bayesian networks. Int. J. Approx. Reasoning 55(1), 84–98 (2014)

    Article  Google Scholar 

  16. Pollino, C.A., Woodberry, O., Nicholson, A., Korb, K., Hart, B.T.: Parameterisation and evaluation of a bayesian network for use in an ecological risk assessment. Environ. Model. Softw. 22(8), 1140–1152 (2007)

    Article  Google Scholar 

  17. Grêt-Regamey, A., Straub, D.: Spatially explicit avalanche risk assessment linking bayesian networks to a GIS. Nat. Hazards Earth Syst. Sci. 6(6), 911–926 (2006)

    Article  Google Scholar 

  18. Straub, D., et al.: Natural hazards risk assessment using bayesian networks. Saf. Reliab. Eng. Systems Struct., 2535–2542 (2005)

    Google Scholar 

  19. Liu, K.F.R., Lu, C.F., Chen, C.W., Shen, Y.S.: Applying Bayesian belief networks to health risk assessment. Stoch. Env. Res. Risk Assess. 26(3), 451–465 (2012)

    Article  Google Scholar 

  20. Weber, P., Medina-Oliva, G., Simon, C., Iung, B.: Overview on bayesian networks applications for dependability, risk analysis and maintenance areas. Eng. Appl. Artif. Intell. 25(4), 671–682 (2012)

    Article  Google Scholar 

  21. Kowalski, J., Kozera, J.: Mapa zagrożeń bezpieczeństwa energetycznego RP w sektorach ropy naftowej i gazu ziemnego. In: Bezpieczeństwo Narodowe, pp. 301–324. BBN, Warszawa (2009)

    Google Scholar 

  22. Alan, H., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)

    Google Scholar 

  23. Eleye-Datubo, A.G., Wall, A., Wang, J.: Marine and offshore safety assessment by incorporative risk modeling in a fuzzy-Bayesian network of an induced mass assignment paradigm. Risk Anal. 28(1), 95–112 (2008)

    Article  Google Scholar 

  24. Friedman, N., Koller, D.: Being Bayesian about network structure. In: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 201–210. Morgan Kaufmann Publishers Inc. (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Poznań University of Economics and Business, Poznań, Poland

    Milena Stróżyna

Authors
  1. Milena Stróżyna
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Milena Stróżyna .

Editor information

Editors and Affiliations

  1. Poznań University of Economics and Business, Poznan, Poland

    Witold Abramowicz

  2. Leipzig University, Leipzig, Germany

    Rainer Alt

  3. Leipzig University, Leipzig, Germany

    Bogdan Franczyk

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Stróżyna, M. (2017). A Bayesian Network Approach to Assessing the Risk and Reliability of Maritime Transport. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-52464-1_34

Download citation

Publish with us

Policies and ethics

Search

Navigation