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.

BACKGROUND The outbreak of sylvatic Yellow Fever (SYF) in humans during 2016-2017 in Brazil is one of the greatest in the history of the disease. The occurrence of the disease in areas with low vaccination coverage favoured the dissemination of the disease; therefore, it is necessary to identify the areas vulnerability to the YF virus (YFV) to assist in the adoption of preventive measures. OBJECTIVE To correlate the physical-environmental elements associated with the occurrence of SYF in humans via a multicriteria analysis. METHODS For the multicriteria analysis, preponderant elements related to SYF occurrences, including soil usage and coverage, temperature, precipitation, altitude, mosquito transmitters, and non-human primate occurrence areas, were considered. The results were validated by assessing the correlation between the incidence of SYF and the vulnerable areas identified in the multicriteria analysis. RESULTS Two regions with different vulnerability to the occurrence of the disease were identified in the multicriteria analysis, with emphasis on the southern areas of the state of São Paulo northeast areas of Minas Gerais, and the entire states of Rio de Janeiro and Espírito Santo. The map of SYF vulnerability obtained in the multicriteria analysis coincides with the areas in which cases of the disease have been recorded. The regions that presented the greatest suitability were in fact the municipalities with the highest incidence. MAIN CONCLUSIONS The multicriteria analysis revealed that the elements that were used are suited for and consistent in the prediction of the areas that are vulnerable to SYF. The results obtained indicate the proximity of the areas that are most vulnerable to the disease to densely populated areas where an Aedes aegypti infestation was observed, which confers a high risk of re-urbanisation of YF.