Publicação
Identification of risk management models and parameters for critical infrastructures
| Resumo: | The resilience of an area/region/country or society is directly related to the performance of its Critical infrastructures (CI), especially when it is affected by extreme events. The increasing number of catastrophic events, such as terrorist attacks or natural disasters (tsunamis, fires, floods), alerted Europe and other nations worldwide to take measures for preventing or reducing possible consequences against these situations. CI are commonly defined as facilities, systems and assets, essential for the maintenance of vital social functions, and their disruption or destruction may significantly impact the well-being of society. It is mandatory for any nation to identify which Infrastructures must be defined as critical, by analyzing the impacts provoked by an extreme event and the society’s dependence towards this Infrastructure. For this purpose, European Commission established a procedure for the identification and designation of European CI ensuring to avoid different approaches within the EU. Three cross-cutting criteria where defined: (a) Casualties; (b) Economic-effect; (c) Public effect. This paper aims to introduce different risk management models for CI and the parameters necessary for quantification of these Methodologies. There are several models for risk management, the ones studied and introduced in this paper were applied in different countries and types of CI, these vary from deterministic approaches to probabilistic methods. The critically parameters are related in governmental, economical, security and welfare terms, these parameters are important for two main reasons: (1) to keep updated the critical index and the maps of risks and vulnerability that predictive models may use; (2) Current tools are essentially based on models weighed by qualitative weights, not allowing the complete analysis of one-off events |
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| Autores principais: | Urbina, Oscar J. |
| Outros Autores: | Teixeira, Elisabete Rodrigues; Matos, José M. |
| Assunto: | Critical infrastructures Extreme Events Risk Management Models Predictive Models Public works Disaster prevention Predictive analytics |
| Ano: | 2021 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The resilience of an area/region/country or society is directly related to the performance of its Critical infrastructures (CI), especially when it is affected by extreme events. The increasing number of catastrophic events, such as terrorist attacks or natural disasters (tsunamis, fires, floods), alerted Europe and other nations worldwide to take measures for preventing or reducing possible consequences against these situations. CI are commonly defined as facilities, systems and assets, essential for the maintenance of vital social functions, and their disruption or destruction may significantly impact the well-being of society. It is mandatory for any nation to identify which Infrastructures must be defined as critical, by analyzing the impacts provoked by an extreme event and the society’s dependence towards this Infrastructure. For this purpose, European Commission established a procedure for the identification and designation of European CI ensuring to avoid different approaches within the EU. Three cross-cutting criteria where defined: (a) Casualties; (b) Economic-effect; (c) Public effect. This paper aims to introduce different risk management models for CI and the parameters necessary for quantification of these Methodologies. There are several models for risk management, the ones studied and introduced in this paper were applied in different countries and types of CI, these vary from deterministic approaches to probabilistic methods. The critically parameters are related in governmental, economical, security and welfare terms, these parameters are important for two main reasons: (1) to keep updated the critical index and the maps of risks and vulnerability that predictive models may use; (2) Current tools are essentially based on models weighed by qualitative weights, not allowing the complete analysis of one-off events |
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