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A hybrid model for learning from failures: the Hurricane Katrina disaster

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There is a need to facilitate learning from failures in the context of natural and man-made disasters. This paper investigates the multi-faceted nature of research in disasters and the aspect of hybrid approaches in modelling within this domain. The paper applies a framework of reliability and multiple criteria decision analysis techniques to the case of the Hurricane Katrina disaster of 2005. It is shown how this hybrid model can be used through an integrative approach to perform a systematic analysis that can lead to learning from failures.

The proposed framework incorporates and integrates Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) analysis and the Risk Priority Number (RPN) concept, together with the Analytic Hierarchy Process (AHP) which is used as a simulation model for decision support. It is shown how the proposed integrated framework can contribute to our understanding of failures and enhances the ability to extract lessons from failures or disasters. Such lessons are then mapped into specific decisions for prevention, and resource allocations, to help avoid a repeat disaster.
Original languageEnglish
Pages (from-to)7869-7881
JournalExpert Systems with Applications
Issue number21
Early online date24 Jun 2015
Publication statusPublished - 30 Nov 2015


  • LABIB_2015_cright_ESA_A Hybrid Model for Learning from Failures the Hurricane Katrina Disaster

    Rights statement: NOTICE: this is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 42(21), (2015), DOI: 10.1016/j.eswa.2015.06.020

    Accepted author manuscript (Post-print), 691 KB, PDF document

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