Using Fuzzy-Improved Principal Component Analysis (PCA-IF) for Ranking of Major Accident Scenarios - H HADEF; M DJEBABRA (Arabian Journal of Science and Engineering, 2020)
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.