Risk analysis of damaged ships: a data-driven Bayesian approach

Kelangath Subin, Purnendu Das, John Quigley, Spyros Hirdaris

Research output: Contribution to journalArticlepeer-review

37 Citations (Scopus)


An accident occurring at sea, though a rare event, has a huge impact both on the economy and the environment. A better and safer shipping practice always demands new ways to improve marine traffic and this essentially requires learning from past experience/faults. In this regard, probabilistic analysis of accidents and associated consequences can play a very important
role in making a better and safer maritime transport system. Bayesian networks represent a class of probabilistic models based on statistics, decision theory and graph theory. This paper introduces the use of data-driven Bayesian modelling in risk analysis and makes a comparison with the different data-driven Bayesian methods available. The data for this study are based on the Lloyds database of accidents from 1997 to 2009. Important influential variables from this database are grouped and a Bayesian network that shows the relationship between the corresponding variables is constructed which in turn provides an insight into probabilistic dependencies existing among the variables in the database and the underlying reasons for these accidents.
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalShips and Offshore Structures
Early online date7 Jul 2011
Publication statusPublished - 2012


  • damage database
  • Bayesian networks
  • risk analysis
  • data-driven Bayesian model
  • damaged ships
  • data-driven
  • Bayesian approach


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