000			02047nab a2200301 i 4500
003			BR-BrBNA
005			20260601103716.0
008			260601b2020 bl.qr|pooa||| 00| 0 eng |
040			_aBR-BrBNA _beng
072			_aK70
100			_aSantos, Eliana Elizabet dos
100			_aSena, Nathalie Cruz
100			_aBalestrin, Diego
100			_aFernandes Filho, Elpidio Inácio
100			_aCosta, Liovando Marciano da
100			_aZeferino, Leiliane Bozzi
245			_aPrediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State
500			_aPublicação on-line; 19 ref.; 1 table; 5 illus.; Summary (En)
520			_a 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.
650			_aINCÊNDIO FLORESTAL
650			_aPROFILAXIA
650			_aMEIO AMBIENTE
650			_aPROTEÇÃO FLORESTAL
773	0		_02929 _9347958 _dRio de Janeiro-RJ Instituto de Florestas - UFRRJ 1994 _o2025-0457 _tFloresta e Ambiente (Brazil) _x1415-0980 / ISSN 2179-8087 0nline _gv. 27(3) p. 1-7; (2020) _wBR2026001341
856			_uhttps://www.scielo.br/j/floram/a/8wNh9YJtKttDHqTMWzwJFpg/?format=pdf&lang=en
942			_cANA
999			_c347966 _d347966