Estimating wildfire ignition probabilities with geographic weighted logistic regression

Marco Marto, Sarah Santos, António Vieira, António Bento-Gonçalves and Filipe Alvelos

Journal of Applied Statistics, 2026, vol. 53, issue 2, 274-303

Abstract: Ignition probabilities play an important role in wildfire-related decision-making and can be included in quantitative approaches for risk management, fuel management and in prepositioning of firefighting resources. We are studying an area around the municipality of Baião in northern Portugal, which frequently experiences fires during the Portuguese fire season. This study can help firefighting authorities identify areas prone to fire and assist them in combating fire occurrences. We estimate fire ignition probabilities using a GWLR model with an exponential kernel, as well as logit and probit link functions. The independent variables used are the population density, the distance to roads, the altitude, the land use (proportion of forest), and the spectral index NDMI (Normalized Difference Moisture Index) from LANDSAT 8. The dependent variable is binary and takes the value 1 if there has been at least one wildfire ignition in a hexagon around each grid point for the decade 2011–2020. Using stratified sampling proportional to the dependent variable values, a training set (70%) and a test set were generated. The results were evaluated with accuracy, an area under the ROC curve, precision, recall, specificity, balanced accuracy and F1. They reveal useful application models, considering the existing reference models for Portugal.

Date: 2026
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DOI: 10.1080/02664763.2025.2511937

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