Predicting wildfire vulnerability using logistic regression and artificial neural networks: a case study in Brazil’s Federal District
Pablo Pozzobon de Bem A , Osmar Abílio de Carvalho Júnior
A C , Eraldo Aparecido Trondoli Matricardi B , Renato Fontes Guimarães A and Roberto Arnaldo Trancoso Gomes A
+ Author Affiliations
- Author Affiliations
A Department of Geography, University of Brasília, Campus Universitário Darcy Ribeiro, 70904-970, Brazil.
B Department of Forest Engineering, University of Brasília, Campus Universitário Darcy Ribeiro, 70910-900, Brazil.
C Corresponding author. Email: osmarjr@unb.br
International Journal of Wildland Fire 28(1) 35-45 https://doi.org/10.1071/WF18018
Submitted: 6 February 2018 Accepted: 5 November 2018 Published: 23 November 2018
Abstract
Predicting the spatial distribution of wildfires is an important step towards proper wildfire management. In this work, we applied two data-mining models commonly used to predict fire occurrence – logistic regression (LR) and an artificial neural network (ANN) – to Brazil’s Federal District, located inside the Brazilian Cerrado. We used Landsat-based burned area products to generate the dependent variable, and nine different anthropogenic and environmental factors as explanatory variables. The models were optimised via feature selection for best area under receiver operating characteristic curve (AUC) and then validated with real burn area data. The models had similar performance, but the ANN model showed better AUC (0.77) and accuracy values when evaluating exclusively non-burned areas (73.39%), whereas it had worse accuracy overall (66.55%) when classifying burned areas, in which LR performed better (65.24%). Moreover, we compared the contribution of each variable to the models, adding some insight into the main causes of wildfires in the region. The main driving aspects of the burned area distribution were land-use type and elevation. The results showed good performance for both models tested. These studies are still scarce despite the importance of the Brazilian savanna.
Additional keywords: data mining classifiers, machine learning, remote sensing, risk, predictive models.
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