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Arabian Journal for Science and Engineering
Published in: Arabian Journal for Science and Engineering 3/2020

13-11-2019 | Research Article - Systems Engineering

Using Fuzzy-Improved Principal Component Analysis (PCA-IF) for Ranking of Major Accident Scenarios

Authors: Hadef Hefaidh, Djebabra Mébarek

Published in: Arabian Journal for Science and Engineering | Issue 3/2020

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Abstract

The industrial risk mapping is a topical problem in the field of risk management that attracts many researchers to develop risk matrices to ensure consultation between their actors. In this context, this paper aims to propose the principal component analysis (PCA) method as support for this consultation. Indeed, the use of PCA method is justified by its robustness for aggregate initial data associated with industrial risks as principal factors and ranking of this risk in terms of their criticalities in risk matrices. However, the aggregation of initial data on industrial risks by the main factors, in some cases, leads to inaccuracies which make it difficult to classify certain risks. This paper proposes two variants of PCA method to solve this inaccuracy and succeeds in classifying risks according to their respective criticalities, namely PCA-Improved (PCA-I) and PCA-I-Fuzzy (PCA-IF). The results come from the PCA application and its proposed variants (PCA-I and PCA-IF) on an example of accident scenarios ranking. We have established a scientific basis for the capitalization of mapping tool for consultation and decision support to industrial risk managers.
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Metadata
Title
Using Fuzzy-Improved Principal Component Analysis (PCA-IF) for Ranking of Major Accident Scenarios
Authors
Hadef Hefaidh
Djebabra Mébarek
Publication date
13-11-2019
Publisher
Springer Berlin Heidelberg
Published in
Arabian Journal for Science and Engineering / Issue 3/2020
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI
https://doi.org/10.1007/s13369-019-04233-7

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