Importance of soil and elevation characteristics for modeling hurricane-induced power outages

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.