2020
DOI: 10.1590/2179-8087.011518
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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 perfor… Show more

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Cited by 4 publications
(2 citation statements)
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“…The Kappa statistic obtained in this study is almost in perfect agreement. It is comparable to and higher than earlier fire prediction studies such as Santos et al [54], who reported a substantial Kappa value of 0.65 for a RF fire prediction model of Minas Gerais, Brazil (2010). Le et al [55] also found a 0.63 kappa value for their proposed deep neural computing model for predicting wildfires in tropical Vietnam.…”
Section: Model Training and Testingsupporting
confidence: 77%
“…The Kappa statistic obtained in this study is almost in perfect agreement. It is comparable to and higher than earlier fire prediction studies such as Santos et al [54], who reported a substantial Kappa value of 0.65 for a RF fire prediction model of Minas Gerais, Brazil (2010). Le et al [55] also found a 0.63 kappa value for their proposed deep neural computing model for predicting wildfires in tropical Vietnam.…”
Section: Model Training and Testingsupporting
confidence: 77%
“…RF, for example, allows for integrating data from different scales and sources, which explains its wide use in many mapping applications based on satellite images [72]. In particular, several studies show the RF potential that is applied to satellite images for the detection of forest fires [82][83][84][85][86][87][88].…”
Section: Introductionmentioning
confidence: 99%