Hurricane Isaac: A Longitudinal Analysis of Storm Characteristics and Power Outage Risk |
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Authors: | Gina L Tonn Seth D Guikema Celso M Ferreira Steven M Quiring |
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Institution: | 1. Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA;2. Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA;3. Department of Civil, Environmental, and Infrastructure Engineering, George Mason University, Fairfax, VA, USA;4. Department of Geography, Texas A&M University, College Station, TX, USA |
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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. |
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Keywords: | Hurricanes power outages random forest |
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