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Major disasters such as wildfire, tornado, hurricane, tropical storm,flooding cause disruptions in infrastructure systems such as power outage,disruption to water supply system, wastewater management, telecommunicationfailures, and transportation facilities. Disruptions in electricityinfrastructures has a negative impact on every sector of a region, such aseducation, medical services, financial, recreation. In this study, we introducea novel approach to investigate the factors which can be associated with longerrestoration time of power service after a hurricane. We consider three types offactors (hazard characteristics, built-environment characteristics, andsocio-demographic factors) that might be associated with longer restorationtimes of power outages during a hurricane. Considering restoration time as thedependent variable and utilizing a comprehensive set of county-level data, wehave estimated a Generalized Accelerated Failure Time (GAFT) that accounts forspatial dependence among observations for time to event data. Consideringspatial correlation in time to event data has improved the model fit by 12%.Using GAFT model and Hurricane Irma as a case study, we examined: (1)differences in electric power outages and restoration rates among differenttypes of power companies: investor-owned power companies, rural and municipalcooperatives; (2) the relationship between the duration of power outage andpower system variables, and socioeconomic attributes. We have found thatfactors such as maximum sustained wind speed, percentage of customers facingpower outage, percentage of customers served by investor-owned power company,median household income, and number of power plants are strongly associatedwith restoration time. This paper identifies the key factors in predicting therestoration time of hurricane-induced power outages.

A Generalized Accelerated Failure Time Model to Predict Restoration Time from Power Outages