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

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.

Automated summary

This paper presents a methodological framework for resilience analysis of interdependent critical infrastructure systems using artificial power and gas networks as an example. Deep learning is used to identify network topology attributes and vulnerability processes. Recovery strategies are proposed and an optimal resilience improvement strategy is obtained through a resilience triangle analysis.