Abstract:

Using a risk matrix for Risk mapping constitutes the basis of risk management strategy. It aims to classify the identified risks with regards to their management and control. This risk classification, which is based on the frequency and the severity dimensions, is often carried out according to a procedure founded on experts’ judgments. In order to overcome the subjectivity bias of this classification, this paper presents the contribution of the Principal Components Analysis (PCA) method: an exploratory method for graphing risks based on factors that allow a better visualized classification of scenarios accidents. Still, the commonly encountered problem in the data classified by the PCA method resides in the main factors of classification; we judged useful to frame these letters by an algebraic formulation to make an improvement of this classification possible. The obtained results show that the suggested method is a promising alternative to solve the recurring problems of risk matrices, notably in accident scenarios’ classification.

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