Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk. Issue 10 (8th February 2016)

• Record Type: Journal Article
• Authors: Tonn, Gina L.; Guikema, Seth D.; Ferreira, Celso M.; Quiring, Steven M.
• Is Part Of: Risk analysis. Volume 36:Issue 10(2016)
• Page Start: 1936
• Page End: 1947
• Publication Date: 2016-02-08
• Subjects: Hurricanes -- power outages -- random forest; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.12552
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract : In August 2012, Hurricane Isaac, a Category 1 hurricane at landfall, caused extensive power outages in Louisiana. The storm brought high winds, storm surge, and flooding to Louisiana, and power outages were widespread and prolonged. Hourly power outage data for the state of Louisiana were collected during the storm and analyzed. This analysis included correlation of hourly power outage figures by zip code with storm conditions including wind, rainfall, and storm surge using a nonparametric ensemble data mining approach. Results were analyzed to understand how correlation of power outages with storm conditions differed geographically within the state. This analysis provided insight on how rainfall and storm surge, along with wind, contribute to power outages in hurricanes. By conducting a longitudinal study of outages at the zip code level, we were able to gain insight into the causal drivers of power outages during hurricanes. Our analysis showed that the statistical importance of storm characteristic covariates to power outages varies geographically. For Hurricane Isaac, wind speed, precipitation, and previous outages generally had high importance, whereas storm surge had lower importance, even in zip codes that experienced significant surge. The results of this analysis can inform the development of power outage forecasting models, which often focus strictly on wind‐related covariates. Our study of Hurricane Isaac indicates that inclusion of other covariates, particularly precipitation, may improve model accuracy and robustness across a range of storm conditions and geography.