Though the 2009 Atlantic hurricane season is not yet officially over so far it has been the mildest in many years, perhaps decades. The relative lull in hurricane activity may be a relief but it’s also, emergency managers are well aware, only a temporary reprieve, at best a window of opportunity to develop enhanced tools to better predict and respond to hurricanes in future, less lucky, years.

A new academic study study by three professors, Seth Guikema of The Johns Hopkins University, Seung-Ryong Han of Korea University, and Steven Quiring of Texas A&M, titled Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models , presents one such potentially breakthrough tool, an improved analytical model for predicting potential power outages and helping utility companies deploy repair crews in advance of hurricanes and other storm events.

“Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas,” the study explains, “ However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas.”

Traditional methods, according to the paper, have used statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired.

In contrast to this the authors propose what they describe as a different type of regression model, which they call a generalized additive model (GAM), which they claim can outperform the types of models used previously. This is done, they say, by developing and validating the new model based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.

The general process for using traditional models in practice for an approaching hurricane, they explain, consisted of “utilizing hurricane track, intensity forecasts, and reconnaissance data from the National Hurricane Center (NHC) or other sources to formulate a small number of scenarios, each consisting of a different estimate of where the hurricane is going, how strong it will be, and what its physical characteristics such as central pressure difference and size will be.”

Forecasters ran a hurricane wind field simulation model for each of the model scenarios selected in the first step to provide a forecast of the time-varying wind field over the area of concern.

While traditional models are reasonably accurate the model suffers, the authors claim, “ from overprediction in the main urban areas combined with underprediction in the rural areas. Examination of the results suggested that this problem of underpredicting rural areas and overpredicting urban areas was likely due to a lack of linearity in the relationship between outages and the miles of overhead line in each grid cell. A formal analysis of these results, including model fit analysis and prediction error analysis based on hold-out sample validation, is given in Han et al. (2009).) The difference in the geographic pattern of the predictions is troubling because one of the two main intended uses of the model is to help the utility company guide the allocation of repair crews between different geographic areas based on relative differences in the predicted number of outages.

The new GAM model on the other hand “ is based on data provided by a large, investor-owned utility company in the central Gulf Coast region based on data from power outages following Hurricanes Katrina (10,105 outages), Ivan (13,568 outages), Dennis (4,840 outages), and other events in the U.S. Gulf Coast region since the mid-1990s.

By taking into account more environmental and power system infrastructure factors than previous analyses , according to the authors, the more comprehensive modeling “can provide more accurate predictions of the number of power outages in each geographic area of a utility company’s service area and a better understanding of the response of the [utility company’s] system.” Using study data, the Generalized Additive Model (GAM) developed for the study outperformed previous models, which tended to overestimate outages in urban zones and underestimate them in rural areas.

As the study explains, “Although this was all of the past outage data that were available, it is a larger data set than is available from many utilities. These outage data were combined with information about the power system, geographic characteristics of the service area, nonhurricane climatic data, and hurricane characteristics from a hurricane wind field simulation model and publicly available hurricane data. While the model developed based on these data is not directly applicable to other areas, it does provide insights into the benefits of a GAM-based approach over a GLM-based approach as well as general insights into the relative influence of the different explanatory variables in the Gulf Coast region of the United States.”

The team wrote, “While there is still error in the predictions, the results provide a much better basis for allocating repair crews among the different geographic portions of the service area.” The analysis is designed to help utility companies develop better power outage estimates and make more effective pre-storm decisions about the number of crews to request through mutual aid agreements, as well as the locations at which crews and materials should be staged in preparation for a recovery effort.