Wildfire risk prediction in Southeastern Mississippi using population interaction

An understanding of how wildfire occurrences are related to human population distribution is important for protecting lives and property, managing timber resources, and conservation of biodiversity. In this study, human population interaction pattern obtained using Newtonʹs gravity model are combined with a forest age and species fuel layer to assess the wildfire potential distribution in the Southeastern Mississippi fire district. Gravity model predictions are compared with a previously developed road density model, and validated against 5 year observational data. Highest human activity patterns are predicted in densely populated areas and are aligned along the highways. The fuel and human activity maps are almost complimentary in the high population region, which validates the intrusive nature of human settlement and their role in redefining land use. The human activity pattern acts as a wildfire ignition source, and the highest wildfire probability is predicted for areas with dense fuel and scattered human populations. The gravity model performs better than the density model for low- and high-risk zones, whereas the latter performs better than the former in medium-risk zones, when compared with the historical data. The study emphasizes the need for improved wildfire prediction models including enhanced depictions of human activity patterns, and the need for integration of a meteorology condition layer.