Risk-Based Criticality Assessment for Smart Critical Infrastructures

Abstract

Today, critical infrastructure is more interconnected, which allows more vulnerabilities in the case of disasters. In addition, the effect of one infrastructure can lead to one or more cascading failures in another infrastructure due to the dependency complexity between them. This article introduces a holistic approach using network indicators and machine learning to better understand the geographical representation of critical infrastructure. Previous work on a similar model was based on a single measure; such as in fashion, this paper introduces four measures utilized to identify the most vital geographic zone in the city. The model aims to increase resilience, focusing on the preparedness phase by assessing the essential nodes of infrastructure, which allows more space to adopt risk mitigation strategies before any disturbance event. Holding in-depth knowledge of the vital zones of small scales and accordingly ranking them will positively improve the overall system resilience.