Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory

Shuliang Wang, Xifeng Gu, Shengyang Luan and Mingwei Zhao

International Journal of Critical Infrastructure Protection, 2021, vol. 35, issue C

Abstract: In this paper, we present a methodological framework for resilience analysis of interdependent critical infrastructure systems and use artificial interdependent power and gas network as an example. We use deep learning to identify network topology attributes and analyze the vulnerability process of interdependent infrastructure systems to different failure scenarios and coupling modes under structural perspective. Then, functional model of the interdependent network is constructed, and the vulnerability process based on functional characteristics is analyzed. At last, we propose different recovery strategies and use a resilience triangle to study the restoration process, and the optimal resilience improvement strategy is acquired from both structural and functional perspectives. The method proposed in this paper can help decision makers develop mitigation techniques and optimal protection strategies.

Keywords: Critical infrastructure systems; Complex network; Deep learning; Failure propagation; Resilience (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ijocip:v:35:y:2021:i:c:s1874548221000500

DOI: 10.1016/j.ijcip.2021.100459

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