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Abstract
Critical infrastructures (CI) are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator GeoPN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.
Keywords
infrastructure interdependency
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asymmetric relations
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disaster management
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GIS
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spatial decision support system
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Qiuming Cheng.
A new mathematical framework and spatial decision support system for modeling cascade interdependency of critical infrastructure during geo-disasters.
Journal of Earth Science, 2017, 28(1): 131-146 DOI:10.1007/s12583-017-0746-4
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