Hurricanes can be a major threat to electrical power systems, often resulting in costly repairs and lengthy restoration times. The Hurricane Outage Prediction Model (HOPM) predicts the location and number of outages for a major coastal Gulf of Mexico utility company up to 5 days before a hurricane makes landfall. This model can help electrical utilities to improve their resource allocations and potentially shorten restoration times. The goal of this study is to evaluate whether the accuracy of the HOPM can be improved by including information on tree species. Our results demonstrate that the model accuracy increased by ∼3% when tree species were included. The most important tree species in our study region were Sweetgum and Loblolly Pine. As the relative abundance of these tree species increased, the number of outages tended to increase. In contrast, increases in the relative abundance of Water Oak and Chestnut Oak did not result in more outages. This suggests that certain oaks may be more resistant to uprooting or snapping when there are strong winds. Therefore, the inclusion of tree species variables in the model provides a means for capturing the spatially varying vulnerability of the trees to strong winds. Even though including tree species improved the accuracy of the model, information about the frequency of tree trimming is more important. Accurate prediction of the location and number of power outages requires information on both the meteorological hazard and the vulnerability of the vegetation.

飓风可能是对电力系统的主要威胁，通常导致昂贵的维修费用和较长的恢复时间。飓风中断预测模型（HOPM）可以预测飓风登陆前5天，墨西哥沿海主要公用事业公司的停机位置和数量。该模型可以帮助电力公司改善其资源分配并可能缩短恢复时间。这项研究的目的是评估是否可以通过纳入有关树种的信息来提高HOPM的准确性。我们的结果表明，包括树木在内的模型精度提高了约3％。在我们研究的区域中，最重要的树种是Sweetgum和Loblolly Pine。随着这些树种相对丰度的增加，中断次数趋于增加。相反，Water Oak和Chestnut Oak的相对丰度并未导致更多的中断。这表明某些橡树在强风下可能更耐连根拔起或折断。因此，在模型中包括树木物种变量提供了一种手段来捕获树木在空间上变化的对强风的脆弱性。即使包含树种可以提高模型的准确性，有关树木修剪频率的信息也更为重要。要准确预测停电的位置和数量，就需要有关气象灾害和植被脆弱性的信息。这表明某些橡树在强风下可能更耐连根拔起或折断。因此，在模型中包含树木物种变量提供了一种手段来捕获树木在强风中的空间变化脆弱性。即使包含树种可以提高模型的准确性，有关树木修剪频率的信息也更为重要。要准确预测停电的位置和数量，就需要有关气象灾害和植被脆弱性的信息。这表明某些橡树在强风下可能更耐连根拔起或折断。因此，在模型中包含树木物种变量提供了一种手段来捕获树木在强风中的空间变化脆弱性。即使包含树种可以提高模型的准确性，有关树木修剪频率的信息也更为重要。要准确预测停电的位置和数量，就需要有关气象灾害和植被脆弱性的信息。有关树木修剪频率的信息更为重要。准确预测停电的位置和次数需要有关气象灾害和植被脆弱性的信息。有关树木修剪频率的信息更为重要。要准确预测停电的位置和数量，就需要有关气象灾害和植被脆弱性的信息。