Handling data uncertainties in event tree analysis

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Author Ferdous, R. en_US
Author Khan, F. en_US
Author Sadiq, R. en_US
Author Amyotte, P. en_US
Author Veitch, B. en_US
Available date 2009-12-27T06:59:13Z en_US
Publication Date 2009 en_US
Citation Refaul Ferdous, Faisal Khan, Rehan Sadiq, Paul Amyotte, Brian Veitch, Handling data uncertainties in event tree analysis, Process Safety and Environmental Protection, Volume 87, Issue 5, September 2009, Pages 283-292 en_US
URI http://dx.doi.org/10.1016/j.psep.2009.07.003 en_US
URI http://hdl.handle.net/10576/10470 en_US
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
Language en en_US
Publisher Elsevier B.V. en_US
Subject Data uncertainties en_US
Subject Fuzzy-based approach en_US
Subject Evidence theory en_US
Subject Event tree analysis en_US
Subject Monte Carlo simulations en_US
Title Handling data uncertainties in event tree analysis en_US
Type Article en_US


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