Importance of soil and elevation characteristics for modeling hurricane-induced power outages
Importance of soil and elevation characteristics for modeling hurricane-induced power outages
复制标题
土壤和海拔特征对于飓风引起的停电建模的重要性
DOI：
10.1007/s11069-010-9672-9
复制
发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
S. Guikema
中科院分区：
文献类型：
--
作者：
S. Quiring;Laiyin Zhu;S. Guikema
飓风可能会严重破坏电力系统，因此，预测即将到来的飓风的潜在影响对于促进规划和应对风暴活动具有重要意义。采用分类和回归树(CART)的数据挖掘方法，评估土壤和地形变量的加入是否提高了停电模型的预测精度。除了飓风、电力系统和环境变量外，共有37个土壤变量和20个地形变量进行了评估。飓风变量，特别是最大阵风和强风持续时间，是所有模型中预测停电的最重要变量。虽然土壤和地形变量的纳入并没有显著提高停电预测的总体准确性，但土壤类型和土壤质地是与飓风相关的停电的有用预测因子。这两个变量都提供了有关土壤稳定性的信息，而土壤稳定性反过来又影响杆子保持直立和树木被连根拔起的可能性。CART还被用来评估环境变量是否可以替代电力系统变量。我们的结果表明，某些土地覆盖变量(例如，Lc21、Lc22和Lc23)是电力系统的合理替代变量，可以用于CART模型，当没有关于电力系统的详细信息时，预测精度只会有很小的下降。因此，在电力系统没有详细信息的地区，可以开发基于CART的停电模型。
Hurricanes can severely damage the electric power system, and therefore, predicting the potential impact of an approaching hurricane is of importance for facilitating planning and storm-response activities. A data mining approach, classification and regression trees (CART), was employed to evaluate whether the inclusion of soil and topographic variables improved the predictive accuracy of the power outage models. A total of 37 soil variables and 20 topographic variables were evaluated in addition to hurricane, power system, and environmental variables. Hurricane variables, specifically the maximum wind gust and duration of strong winds, were the most important variables for predicting power outages in all models. Although the inclusion of soil and topographic variables did not significantly improve the overall accuracy of outage predictions, soil type and soil texture are useful predictors of hurricane-related power outages. Both of these variables provide information about the soil stability which, in turn, influences the likelihood of poles remaining upright and trees being uprooted. CART was also used to evaluate whether environmental variables can be used instead of power system variables. Our results demonstrated that certain land cover variables (e.g., LC21, LC22, and LC23) are reasonable proxies for the power system and can be used in a CART model, with only a minor decrease in predictive accuracy, when detailed information about the power system is not available. Therefore, CART-based power outage models can be developed in regions where detailed information on the power system is not available.