A review of single and multi-hazard risk assessment approaches for critical infrastructures protection

One of the greatest societal challenges is represented by Critical Infrastructures (CIs) protection. To minimize the impacts of man-made and natural threats, a series of risk assessment techniques have been developed. This work aims to critically compare state-of-the-art risk assessment methodologies for CIs protection, to find the pros and cons of each of them. The paper firstly defines the main challenges in performing the risk assessment of CIs, which have been identified in data availability and in modelling multiple hazard interactions. Afterwards, twelve different risk evaluation methodologies, including mathematical and statistical methods, machine learning techniques, graph and network methods, are analyzed and compared. Every method is described and its strengths and weaknesses are summarized in a suitable Table. Results show that statistical and mathematical methods provide the most accurate results, but need a large amount of data and execution time, while machine learning and complex network approaches work well even if the data are scarce and have a lower computational cost. In addition, the graph and network approaches tend to be the most flexible, able to adapt to every data availability condition and to deal with multiple hazards contemporarily.