Towards probabilistic models for the prediction of a ship performance in dynamic ice

Cold Regions Science and Technology

Volumn 112, Issue , 2015, Pages 14-28

  Montewka, Jakub ^a   Goerlandt, Floris ^a   Kujala, Pentti ^a   Lensu, Mikko ^b  

^a AALTO UNIVERSITY   (Finland)

^b FINNISH METEOROLOGICAL INSTITUTE   (Finland)

Author keywords

Bayesian networks; Machine learning; Ship beset in ice; Ship performance in ice

Indexed keywords

AUTOMATION; BAYESIAN NETWORKS; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; SHIPS; VEHICLE PERFORMANCE;

AUTOMATIC IDENTIFICATION SYSTEM; DATA-DRIVEN MODEL; ICE CONDITIONS; ICE COVERED WATERS; JOINT EFFECT; PROBABILISTIC MODELS; SEMIEMPIRICAL MODELS; SHIP PERFORMANCE;

ICE;

ARTIFICIAL INTELLIGENCE; BAYESIAN ANALYSIS; ICE COVER; ICE THICKNESS; NUMERICAL MODEL; PROBABILITY; VESSEL;

EID: 84921468785     PISSN: 0165232X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.coldregions.2014.12.009     Document Type: Article

References (39)

1. Acid S., de Campos L.M., Fernández-Luna J.M., Rodríguez S., María Rodríguez J., Luis Salcedo J. A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service. Artif. Intell. Med. Mar. 2004, 30(3):215-232.
2. Cheng J., Greiner R. Learning Bayesian Belief Network Classifiers: algorithms and system. Advances in Artificial Intelligence SE - 14 2001, vol. 2056:141-151. Springer Berlin Heidelberg. E. Stroulia, S. Matwin (Eds.).
3. Darwiche A. Modeling and reasoning with Bayesian networks 2009, Cambridge University Press. 1st ed.
4. E.T.S.Inc, G.S.Ltd, McCallum J. Safe speed in ice: an analysis of transit speeds and ice decision numerals 1996, Enfotec Technical Services INC, Ottawa.
5. Goerlandt F., Montewka J. A probabilistic model for accidental cargo oil outflow from product tankers in a ship-ship collision. Mar. Pollut. Bull. 2014, 79(1-2):130-144.
6. Guinness R.E., Saarimaki J., Ruotsalainen L., Kuusniemi H., Goerlandt F., Montewka J., Berglund R., Kotovirta V. A method for ice-aware maritime route optimization". IEEE/ION PLANS 2014 2014, 1371-1378.
7. Haapala J., Lönnroth N., Stössel A. A numerical study of open water formation in sea ice". J. Geophys. Res. Oceans 2005, vol. 110(no. C9). (p. n/a-n/a).
8. Hänninen M. Bayesian networks for maritime traffic accident prevention: benefits and challenges. Accid. Anal. Prev. Sep. 2014, 73C:305-312.
9. Hänninen M., Valdez Banda O.A., Kujala P. Bayesian network model of maritime safety management. Expert Syst. Appl. Dec. 2014, 41(17):7837-7846.
10. Juva M., Riska K. On the power requirement in the Finnish-Swedish ice class rules 2002, Finnish Maritime Administration, Espoo, Finland.
11. Keinonen A. Icebreaker characteristics synthesis 1996, Transport Canada, Ottawa.
12. Kjræulff U.B., Madsen A.L. Bayesian networks and influence diagrams: a guide to construction and analysis: a guide to construction and analysis (Google eBook) 2012, vol. 2012:399. Springer.
13. K. B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence, Second Edition (Chapman & Hall/CRC Computer Science & Data Analysis). CRC Press, 2010, p. 491.
14. Kotovirta V., Jalonen R., Axell L., Riska K., Berglund R. A system for route optimization in ice-covered waters. Cold Reg. Sci. Technol. Jan. 2009, 55(1):52-62.
15. Kubat I. Quantifying ice pressure conditions and predicting the risk of ship besetting. International Conference and Exhibition on Performance of Ships and Structures in Ice 2012 (ICETECH 2012) 2012.
16. Kubat I., Babael H., Sayed M. Analysis of besetting incidents in Frobisher Bay during 2012 shipping season. Proceeding of International Conference on Port and Ocean Engineering undere Arctic Conditions (POAC) 2013.
17. Kubat I., Fowler C.D., Sayed M. Snow and ice-related hazards, risks and disasters 2015, 647-676. Elsevier.
18. Külaots R., Kujala P., von Bock und Polach R., Montewka J. Modelling of ship resistance in compressive ice channels". Proceedings of the 22nd International Conference on Port and Ocean Engineering under Arctic Condition 2013.
19. Lapp D.J., Keinonen A.J., King D.H. Calibration of the Arctic marine transportation simulation model 1997.
20. LaPrairie D., Wilhelmson M., Riska K. Transit simulation model for ships in Baltic ice conditions. Documentation of the calculated routine 1995.
21. Lehikoinen A., Luoma E., Mäntyniemi S., Kuikka S. Optimizing the recovery efficiency of Finnish oil combating vessels in the Gulf of Finland using Bayesian Networks. Environ. Sci. Technol. Feb. 2013, 47(4):1792-1799.
22. Lehtiranta J., Lensu M., Haapala J. Ice model validation on local scale 2012, Deliverable 3.2, SAFEWIN project, FP7-RTD- 233884, Helsinki.
23. Lindqvist G. A straightforward method for calculation of ice resistance of ships" 1989, 722-735.
24. Lubbad R., Løset S. A numerical model for real-time simulation of ship-ice interaction. Cold Reg. Sci. Technol. Feb. 2011, 65(2):111-127.
25. Madsen A.L., Lang M., Kjærulff U.B., Jensen F. The Hugin Tool for learning Bayesian Networks. ECSQARU 2003, LNAI 2711 2003, 594-605.
26. 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. 2014, 124:142-157.
27. Mulherin N.D., Eppler D.T., Proshutinsky T.O., Proshutinsky A.Y., Farmer D., Smith O.P. Development and results of a northern sea route transit model" 1996, {US} Army Corps of Enigineers, {CRREL}.
28. Naegle J.N. Ice-resistance prediction and motion simulation for ships operating in the continuous model of icebreaking 1980, The University of Michigan, Michigan, {USA}.
29. Nielsen T.D., Jensen F.V. Bayesian networks and decision graphs (information science and statistics) 2007, 447. Springer.
30. Pitchforth J., Mengersen K. A proposed validation framework for expert elicited Bayesian networks. Expert Syst. Appl. Jan. 2013, 40(1):162-167.
31. Riska K., Wilhelmson M., Englund K., Leiviskä T. Performance of merchant vessels in ice in the Baltic 1997, Winter Navigation Research Board, Helsinki.
32. SMHI Ice chart for the Baltic Sea for 6th of March 2011" 2011, SMHI, (Mar).
33. P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, Second Edition (Adaptive Computation and Machine Learning). A, Bradford Book, 2001, p. 568.
34. Straub D., Grêt-Regamey A. A Bayesian probabilistic framework for avalanche modelling based on observations. Cold Reg. Sci. Technol. Dec. 2006, 46(3):192-203.
35. Su B., Riska K., Moan T. A numerical method for the prediction of ship performance in level ice. Cold Reg. Sci. Technol. Mar. 2010, 60(3):177-188.
36. Tomac T., Klanac A., Katalinic M., Ehlers S., von B.und Polach R., Suominen M., Montewka J. Numerical simulations of ship resistance in model ice". Developments in Maritime Transportation and Exploitation of Sea Resources 2013, 847-851. CRC Press. C. Guedes Soares, L. Pena (Eds.).
37. TRAFI Maritime safety regulation. Ice class regulations and the application thereof" 2010, Transport Safety Agency, Helsinki, Finland, (Nov).
38. US Department of Homeland Security Class A AIS position report" [Online]. Available:, (Accessed: 25-Mar-2013). http://www.navcen.uscg.gov/?pageName=AISMessagesA.
39. Zhang H. The optimality of Naive Bayes 2004.