Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD, USA.
Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA.
Risk Anal. 2016 Oct;36(10):1936-1947. doi: 10.1111/risa.12552. Epub 2016 Feb 8.
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.
2012 年 8 月,登陆时为 1 级飓风的艾萨克飓风(Hurricane Isaac)导致路易斯安那州大面积停电。这场风暴给路易斯安那州带来了大风、风暴潮和洪水,停电范围广泛且持续时间长。在风暴期间收集了路易斯安那州的每小时停电数据并进行了分析。该分析包括使用非参数集成数据挖掘方法按邮政编码对每小时停电数据与风、降雨和风暴潮等风暴条件进行相关性分析。结果进行了分析,以了解州内不同地区停电与风暴条件的相关性差异。该分析深入了解了降雨和风暴潮与风一起如何导致飓风停电。通过对邮政编码级别的停电进行纵向研究,我们能够深入了解飓风期间停电的因果驱动因素。我们的分析表明,风暴特征协变量对停电的统计重要性在地理上有所不同。对于艾萨克飓风,风速、降水和先前的停电通常具有较高的重要性,而风暴潮的重要性较低,即使在经历了重大风暴潮的邮政编码中也是如此。该分析的结果可以为停电预测模型的开发提供信息,这些模型通常严格关注与风相关的协变量。我们对艾萨克飓风的研究表明,纳入其他协变量,特别是降水,可能会提高模型在各种风暴条件和地理环境下的准确性和稳健性。