02047nab a2200301 i 4500 BR-BrBNA 20260601103716.0 260601b2020 bl.qr|pooa||| 00| 0 eng | BR-BrBNA eng K70 Santos, Eliana Elizabet dos Sena, Nathalie Cruz Balestrin, Diego Fernandes Filho, Elpidio Inácio Costa, Liovando Marciano da Zeferino, Leiliane Bozzi Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State Publicação on-line; 19 ref.; 1 table; 5 illus.; Summary (En) Abstract Fire behavior prediction models can assist environmental agencies with fire prevention and control. This study aimed to adjust a fire prediction model for the state of Minas Gerais, Brazil. Using the R program and hotspots provided by the National Institute for Space Research (INPE) for 2010, prediction of the probability of fires through the Random Forest algorithm was conducted using the Bootstrapping method. The model generated a prediction map with global kappa value of 0.65. External validation was performed with hotspots in 2015. Results showed that 58% of the hotspots are in areas with ignition probability > 50%, being 24% of them in areas with 25-50% probability, and 17% in areas with < 25% probability. These results were considered satisfactory, demonstrating that the model is suitable for predicting fires. Keywords: fires; modeling; environmental monitoring. INCÊNDIO FLORESTAL PROFILAXIA MEIO AMBIENTE PROTEÇÃO FLORESTAL 2929 347958 Rio de Janeiro-RJ Instituto de Florestas - UFRRJ 1994 2025-0457 Floresta e Ambiente (Brazil) 1415-0980 / ISSN 2179-8087 0nline v. 27(3) p. 1-7; (2020) BR2026001341 https://www.scielo.br/j/floram/a/8wNh9YJtKttDHqTMWzwJFpg/?format=pdf&lang=en ANA 347966 347966