Multi-Dimensional Infrastructure Resilience Modeling: An Application to Hurricane-Prone Electric Power Distribution Systems

Despite the scientific consensus on the multivariate nature of resilience, the majority of the existing approaches either focus on modeling a single dimension of resilience, or model its various dimensions separately. In this paper, we propose leveraging one of the most recent advances in statistical machine learning to characterize the multivariate inoperability of an electric power distribution system as a non-linear function of the system's topology, hurricane hazard characteristics, and the service area's climate and topography. The model can then be used as a predictive tool to assess various investment strategies for enhancing the multivariate resilience of the system. The results established the number of customers served, tree-trimming frequency, hurricane intensity, land-cover types, and soil moisture as the key predictors of the distribution system's multivariate inoperability. The variable influence heat-map helped identify the clusters of predictors that jointly influence one or more measures of inoperability. Moreover, the partial dependence plots were leveraged to examine the non-linear relationships between the focal predictors and the various measures of hurricane impact. The estimated multivariate inoperability model was then used to assess resilience enhancement strategies. The proposed approach can help infrastructure managers and urban planners to approximate the multivariate resilience of infrastructure holistically, predict the system's resilience under various stochastic perturbation regimes, and identify effective strategies to improve the overall resilience of the system.