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World class computing and information science research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by researchers from the Department of Computer & Information Sciences involved in mathematically structured programming, similarity and metric search, computer security, software systems, combinatronics and digital health.

The Department also includes the iSchool Research Group, which performs leading research into socio-technical phenomena and topics such as information retrieval and information seeking behaviour.

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

Subin, Kelangath and Das, Purnendu and Quigley, John and Hirdaris, Spyros (2012) Risk analysis of damaged ships : a data-driven Bayesian approach. Ships and Offshore Structures. pp. 1-15. ISSN 1754-212X

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Abstract

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.