Handling data uncertainties in event tree analysis

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Handling data uncertainties in event tree analysis

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dc.contributor.author Ferdous, R.
dc.contributor.author Khan, F.
dc.contributor.author Sadiq, R.
dc.contributor.author Amyotte, P.
dc.contributor.author Veitch, B.
dc.date.accessioned 2009-12-27T06:59:13Z
dc.date.available 2009-12-27T06:59:13Z
dc.date.issued 2009
dc.identifier.citation Volume 87, Issue 5, September 2009, Pages 283-292 en_US
dc.identifier.uri http://dx.doi.org/10.1016/j.psep.2009.07.003
dc.identifier.uri http://hdl.handle.net/10576/10470
dc.description.abstract Event tree analysis (ETA) is an established risk analysis technique to assess likelihood (in a probabilistic context) of an accident. The objective data available to estimate the likelihood is often missing (or sparse), and even if available, is subject to incompleteness (partial ignorance) and imprecision (vagueness). Without addressing incompleteness and imprecision in the available data, ETA and subsequent risk analysis give a false impression of precision and correctness that undermines the overall credibility of the process. This paper explores two approaches to address data uncertainties, namely, fuzzy sets and evidence theory, and compares the results with Monte Carlo simulations. A fuzzy-based approach is used for handling imprecision and subjectivity, whereas evidence theory is used for handling inconsistent, incomplete and conflicting data. Application of these approaches in ETA is demonstrated using the example of an LPG release near a processing facility. en_US
dc.language.iso en en_US
dc.subject Data uncertainties en_US
dc.subject Fuzzy-based approach en_US
dc.subject Evidence theory en_US
dc.subject Event tree analysis en_US
dc.subject Monte Carlo simulations en_US
dc.title Handling data uncertainties in event tree analysis en_US
dc.type Article en_US

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