Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/77895

TítuloIdentification of risk management models and parameters for critical infrastructures
Autor(es)Urbina, Oscar J.
Teixeira, Elisabete Rodrigues
Matos, José M.
Palavras-chaveCritical infrastructures
Extreme Events
Risk Management Models
Predictive Models
Public works
Disaster prevention
Predictive analytics
Data12-Mai-2021
EditoraSpringer Science+Business Media
RevistaLecture Notes in Civil Engineering
Resumo(s)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
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/77895
ISBN9783030736156
DOI10.1007/978-3-030-73616-3_29
ISSN2366-2557
Arbitragem científicayes
AcessoAcesso restrito UMinho
Aparece nas coleções:ISISE - Comunicações a Conferências Internacionais

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