Advertisement

Decision Support Tool Employing Bayesian Risk Framework for Environmentally Safe Shipping

  • Sotirios GyftakisEmail author
  • Ioanna Koromila
  • Theodore Giannakopoulos
  • Zoe Nivolianitou
  • Eleni Charou
  • Stavros Perantonis
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 131)

Abstract

Due to the significant increase of tanker traffic from and to the Black Sea that pass through narrow straits formed by the 1600 Greek islands, the Aegean Sea is characterized by an extremely high marine environmental risk. Therefore it is vital to all socio-economic and environmental sectors to reduce the risk of a ship accident in that area. In this chapter a web tool for environmentally safe shipping is presented. The proposed tool focuses on extracting aggregated statistics using spatial analysis of multilayer information: vessel trajectories, vessel data as well as information regarding environmentally important areas. The decision support system includes preprocessing, clustering of trajectories (based on their spatial similarity) and risk assessment employing probabilistic models (Bayesian network). Applications of the web tool are presented in areas such as marine traffic monitoring in environmentally protected areas, and influence of restricted areas in marine traffic. Results demonstrate that the web tool can provide essential information for maritime policy makers.

Keywords

Marine safety Marine traffic monitoring Risk analysis Bayesian network Dynamic risk model Vessel trajectories classification and visualization Spatiotemporal trajectories analysis 

Notes

Acknowledgements

This work was carried out in the framework of the project “AMINESS: Analysis of Marine Information for Environmentally Safe Shipping” that was co-financed by the European Fund for Regional Development and by Greek National funds through the operational programs “Competitiveness and Entrepreneurship” and “Regions in Transition” of the National Strategic Reference Framework—Action: “COOPERATION 2011 Partnerships of Production and Research Institutions in Focused Research and Technology Sectors”.

References

  1. 1.
    Bingham, P., Koch, L.: Liner shipping in the european union. Technical report, The World Shipping Council and IHS Global Insight (2009)Google Scholar
  2. 2.
    Det Norske Veritas (DNV): Formal safety assessment—Large passenger ships, annex ii: Risk assessment large passenger ships—Navigation. Technical report, Det Norske, Veritas (2003)Google Scholar
  3. 3.
    Etkin, D.: Estimating clean-up costs for oil spills. In: International Oil Spill Conference, American Petroleum Institute. Washington, DC (1999)Google Scholar
  4. 4.
    European Environment Agency: Nationally designated areas (CDDA) (2016a). http://www.eea.europa.eu/data-and-maps/data/nationally-designated-areas-national-cdda-10
  5. 5.
    European Environment Agency: Natura 2000 data—The European network of protected sites (2016b). http://www.eea.europa.eu/data-and-maps/data/natura-7
  6. 6.
    Giannakopoulos, T., Gyftakis, S., Charou, E., Perantonis, S., Nivolianitou, Z., Koromila, I., Makrygiorgos, A.: Long-term marine traffic monitoring for environmental safety in the Aegean Sea. In: 36th International Symposium on Remote Sensing of Environment. Berlin, Germany, May 2015Google Scholar
  7. 7.
    Giannakopoulos, T., Vetsikas, I., Koromila, I., Karkaletsis, V., Perantonis, S.: Aminess: a platform for environmentally safe shipping. In: 7th International Conference on PErvasive Technologies Related to Assistive Environments. Rhodes, Greece, May 2014Google Scholar
  8. 8.
  9. 9.
    Google: Keyhole Markup Language (2016b). https://developers.google.com/kml/documentation/kml_tut
  10. 10.
    Grey, C.: The cost of oil spills from tankers: an analysis of iopc fund incidents. In: International Oil Spill Conference. vol. 1, pp. 41–47. American Petroleum Institute (1999)Google Scholar
  11. 11.
    Gyftakis, S., Giannakopoulos, T., Makrygiorgos, A., Charou, E., Perantonis, S., Koromila, I., Nivolianitou, Z.: A maritime data analytics platform for policy recommendation. In: 6th International Conference on Information, Intelligence, Systems and Applications. Corfu, Greece, July 2015Google Scholar
  12. 12.
    Hanninen, M.: Bayesian networks for maritime traffic accident prevention: Benefits and challenges. Accid. Anal. Prev. 73, 305–312 (2014)CrossRefGoogle Scholar
  13. 13.
    Hanninen, M., Kujala, P.: Bayesian network modeling of port state control inspection findings and ship accident involvement. Expert Syst. Appl. 41(4), 1632–1646 (2014)CrossRefGoogle Scholar
  14. 14.
    IMO: International maritime organization. msc 83/inf.2. formal safety assessment: Consolidated text of the guidelines for formal safety assessment (fsa) for use in the imo rule-making process (2009). (msc/circ.1023mepc/circ.392)Google Scholar
  15. 15.
    ITOPF: Tip 13: Effects of oil pollution on the marine environment. Technical report, International Tanker Owners Pollution Federation (2014)Google Scholar
  16. 16.
    Jensen, J., Soares, C., Papanikolaou, A.: Methods and tools. In: Papanikolaou, A. (ed.) Risk-Based Ship Design: Methods, Tools and Applications, pp. 213–231. Springer, Berlin, Heidelberg (2009)Google Scholar
  17. 17.
    Jiacai, P., Qingshan, J., Zheping, S., Jinxing, H.: An ais data visualization model for assessing maritime traffic situation and its applications. Proc. Eng. 29, 365–369 (2012)CrossRefGoogle Scholar
  18. 18.
    Kisilevich, S., Mansmann, F., Nanni, M., Rinzivillo, S.: Spatio-temporal clustering: a survey. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd ed, pp. 855–874. Springer Science+Business Media (2010)Google Scholar
  19. 19.
    Kontovas, C., Psaraftis, H., Ventikos, N.: An empirical analysis of iopcf oil spill cost data. Mar. Pollut. Bull. 60, 1455–1466 (2010)CrossRefGoogle Scholar
  20. 20.
    Koromila, I., Nivolianitou, Z., Giannakopoulos, T.: Bayesian network to predict environmental risk of a possible ship accident. In: 7th International Conference on PErvasive Technologies Related to Assistive Environments. Rhodes, Greece, May 2014Google Scholar
  21. 21.
    Koromila, I., Nivolianitou, Z., Giannakopoulos, T., Perantonis, S., Charou, E., Gyftakis, S.: A dynamic model for environmentally safe shipping through the Aegean Sea. In: 6th International Conference on Information, Intelligence, Systems and Applications. Corfu, Greece, July 2015Google Scholar
  22. 22.
    Liu, X., Wirtz, K.: Total oil spill costs and compensations. Marit. Policy and Manage. 33(1), 49–60 (2006)CrossRefGoogle Scholar
  23. 23.
    Makrygiorgos, A., Giannakopoulos, T., Perantonis, S.: Accelerating multi-objective ship routing using a novel grid structure and a simple heuristic. In: 1st International Workshop on Modelling, Computing and Data Handling for Marine Transportation, IISA 2015. Corfu, Greece, July 2015Google Scholar
  24. 24.
    Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K., Kujala, P.: A framework for risk assessment for maritime transportation systems—A case study for open sea collisions involving ropax vessels. Reliab. Eng. Syst. Saf. 124, 142–157 (2014)CrossRefGoogle Scholar
  25. 25.
    Müllner, D.: fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python. J. Statist. Softw. 53(1), 1–18 (2013)Google Scholar
  26. 26.
    Pelekis, N., Frentzos, E., Giatrakos, N., Theodoridis, Y.: Hermes: A trajectory db engine for mobility-centric applications. In: ACM SIGMOD International Conference on Management of Data. Vancouver, Canada, June 2008Google Scholar
  27. 27.
    Pelekis, N., Frentzos, E., Giatrakos, N., Theodoridis, Y.: Hermes: A trajectory db engine for mobility-centric applications. Int. J. Knowl. Based Organ. 5(2), 19–41 (2015)CrossRefGoogle Scholar
  28. 28.
    Silveira, P., Teixeira, A., Soares, C.: Use of ais data to characterize marine traffic patterns and ship collision risk off the coast of portugal. J. Navig. 66(6), 879–898 (2013)CrossRefGoogle Scholar
  29. 29.
    White, I., Molloy, F.: Factors that determine the cost of oil spills. In: International Oil Spill Conference. Vancouver, Canada (2003)Google Scholar
  30. 30.
  31. 31.
    Willems, N., Wetering, H.V.D., Wijk, J.V.: Visualization of vessel movements. Comput. Graph. Forum 28(3), 959–966 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sotirios Gyftakis
    • 1
    Email author
  • Ioanna Koromila
    • 2
    • 3
  • Theodore Giannakopoulos
    • 1
  • Zoe Nivolianitou
    • 2
  • Eleni Charou
    • 1
  • Stavros Perantonis
    • 1
  1. 1.Institute of Informatics and Telecommunications, National Centre for Scientific Research “DEMOKRITOS”Aghia ParaskeviGreece
  2. 2.Institute of Nuclear and Radiological Sciences and Technology, Energy and Safety, National Centre for Scientific Research “DEMOKRITOS”Aghia ParaskeviGreece
  3. 3.Ship Dynamics, Stability and Safety Research Group, Department of Naval Architecture and Marine EngineeringNational Technical University of AthensAthensGreece

Personalised recommendations