Burning Susceptibility Modeling to Reduce Wildfire Impacts: A GIS and Multivariate Statistics Approach

Abstract

Forest burning susceptibility mapping is a tool to mitigate wildfires, with several methods to develop them. This study aimed to compare the Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), and Random Forest (RF) methods for mapping. Several variables were used to generate the maps. For MLR and RF methods, fire frequency between 1990 and 2010 was used as the response variable in the models. To validate the methods (AHP, MLR and RF), fire data between 2011 and 2018 were used in four stages. RF was the best method employed. Correct and incorrect values for this method were 74% and 26% and AUC 0.66. The sensitivity and specificity for the highest risk class were 31% and 96%. The low sensitivity values can be attributed to the randomness attributed to anthropic fire. The high specificity values point to a good separation of the higher risk class compared to the others.