Tropical cyclones are the leading cause of major power outages in the U.S., and their effects can be devastating for communities. However, few studies have holistically examined the degree to which socio-economic variables can explain spatial variations in disruptions and reveal potential inequities thereof. Here, we apply machine learning techniques to analyze 20 tropical cyclones and predict county-level outage duration and percentage of customers losing power using a comprehensive set of weather, environmental, and socio-economic factors. Our models are able to accurately predict these outage response variables, but after controlling for the effects of weather conditions and environmental factors in the models, we find the effects of socio-economic variables to be largely immaterial. However, county-level data could be overlooking effects of socio-economic disparities taking place at more granular spatial scales, and we must remain aware of the fact that when faced with similar outage events, socio-economically vulnerable communities will still find it more difficult to cope with disruptions compared to less vulnerable ones.

The U.S. inland waterways play a vital role in the domestic economy, but extreme weather events, especially floods, perennially threaten to disrupt their operations. Here, we develop a data-driven approach to analyzing economic risks due to flood closures along the inland waterways that combines agent-based, economic interdependence, and Bayesian modeling. We demonstrate our framework by evaluating economic impacts of various flood disruptions along the Upper Mississippi River and determining cases where a publicly operated, flood-resilient port located near the mouth of the river can reroute shipments and mitigate production losses for the region. We find that Illinois, Louisiana, Minnesota, and Missouri are the states that suffer the most production losses from flood disruptions and that agriculture and chemical manufacturing are the most impacted industries. However, during floods whose return periods exceed 30-years, the flood resilient port becomes cost-effective in mitigating losses for the region. Our methodology can be easily extended to other hazards and sections of the inland waterways.