The electric power infrastructure in Puerto Rico suffered substantial damage as Hurricane Maria crossed the island on September 20, 2017. Despite significant efforts made by authorities, it took almost a year to achieve near-complete power recovery. In this study, we used spaceborne daily nighttime lights (NTL) imagery as a surrogate measure of power loss and restoration. We quantified the spatial and temporal extent of loss of electric power and trends in gradual recovery at the 889 county subdivisions for over eight months and computed days without service at the above tabulation areas. We formulated a Quasi-Poisson regression model to identify the association of the features from physical and socioeconomic domains with the power recovery effort. According to the model, the recovery time extended for areas closer to the landfall location of the hurricane, with every 50-kilometer increase in distance from the landfall corresponding to 30% fewer days without power (95% CI = 26% - 33%). Road connectivity was a major issue for the restoration effort, areas having a direct connection with hi-speed roads recovered more quickly with 7% fewer outage days (95% CI = 1% - 13%). The areas which were affected by moderate landslide needed 5.5% (95% CI = 1% - 10%), and high landslide areas needed 11.4% (95% CI = 2% - 21%) more days to recover. Financially disadvantaged areas suffered more from the extended outage. For every 10% increase in population below the poverty line, there was a 2% increase in recovery time (95% CI = 0.3% - 2.8%).

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.