Forecasting Approaches
Read this article. Two forecasting approaches are employed for forest fire disaster response planning. Focus on the qualitative flow chart in Figure 2.
Conclusions
In this study, quantitative FR and qualitative AHP techniques were applied to map forest fire-prone areas in Bhutan and were compared in terms of accuracy of mapping. Seven forest fire conditioning factors, comprising LULC, distance from human settlement, distance from road, distance from the southern international border, aspect, elevation, and slope, were utilized for predicting fire-prone areas in Bhutan. The map produced by the FR model was found to be more accurate (with an 82% prediction rate). Therefore, we suggest using this to reliably predict forest fires and allocate resources to fire-prone areas for any prevention and suppression activities. MODIS forest fire points were found to be not reliable in predicting forest fires in Bhutan due to a high percentage of overlap with 'low' fire-prone areas. Hence, we caution their use for predicting forest fires. The forest fire-prone area maps generated in this research will be useful for the Bhutan Government to improve its current National Forest Fire Management Strategy.