# Forecasting the Impacts of Prescribed Fires
## 5 Conclusions  
An analysis of the GFC burn permit database for the years 2010-2014 revealed that the season and weather play important roles in the burn/no-burn decisions made by land managers. Acreage treated by fire increases in February and March and when weather conditions are more favorable for conducting safe and effective burns. This triggered the idea of forecasting burn activity based on the time of the year and the weather forecast. A PB activity forecasting model was developed using the CART method with the daily burn areas by county from GFC's burn permit database and meteorological parameters from the closest fire weather monitors. The number of predictor variables was limited to circumvent over-modeling. The model was trained with statewide data for the years 2010-2014 and evaluated with 2015 data. Four metrics were used for evaluation: (1) the accuracy or the overall correctness of the model; (2) the precision or the accuracy in predicting burn days; (3) the recall or the number of correctly predicted burn days over the number of burn days; and (4) the \(F1\) score, which is the harmonic mean of precision and recall. Later, considering the geographic variation in the demand for burning, this statewide model was replaced with county-specific models (a different model for each of the 159 counties in Georgia). The \(F1\) score of the burn forecasts for 2015 improved significantly with the county-specific models, showing their clear advantage over a single model for the entire state. In addition, because of the strong seasonality associated with the amount of PB, we started to employ different daily average burn areas for each month, instead of one average for the entire burn season. If the burn-forecast model predicts a burn day, the daily average burn area for the county is split into burns of typical size, which are distributed randomly to forested areas. Then, the amount of fuel that these burns would consume is estimated using FCCS fuel load maps and fuel moisture data from the fire weather network. Fire emissions are then calculated using emission factors that are characteristic of Southeastern fuels and inpu't to the HiRes2 air quality forecasting system. HiRes2 provides forecasts of not only air quality (O\({}_{3}\) and PM\({}_{2.5}\)) but also impacts of PB on air quality, using the DDM sensitivity analysis method. The forecasting skills of the PB impact prediction system were evaluated for the 2015 and 2016 burn seasons (January-April). The burn forecasts were evaluated qualitatively every day against the NOAA HMS Fire and Smoke Analyses for agreement in regard to the location and density of the fires, and quantitatively, at the end of the burn season, against burn areas permitted by GFC. In 2015, the correlation between the predicted and permitted daily statewide total burn areas was stronger with the county-specific models than with the statewide model. However, even with the better performing county-specific models, both the daily statewide total and the countywide seasonal total burn areas were generally underestimated, with some overestimations for low burn activity days or counties and large underestimations for high activity days or counties. In 2016, the underestimation was more severe than in 2015, probably due to the ENSO conditions at the beginning of the year. Burn impact forecasts were evaluated using observations of possible PB impacts by the statewide air quality monitoring network. In 2016, the precision, recall and \(F1\) score for correctly predicting the PB impacts over 32 \(\upmu\)g m\({}^{-3}\) (the 95 th percentile of all observed afternoon PM\({}_{2.5}\) concentrations at Georgia's PM\({}_{2.5}\) monitors in 2016) were equal to 19%, 36%, and 25%, respectively. An analysis of the correlation between forecast biases showed that the bias in burn impact forecast could be reduced if the burn area forecast was improved.