Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State
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 201...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Universidade Federal Rural do Rio de Janeiro
2020-06-01
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| Series: | Floresta e Ambiente |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000300119&tlng=en |
| _version_ | 1818973642436902912 |
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| author | Eliana Elizabet dos Santos Nathalie Cruz Sena Diego Balestrin Elpidio Inácio Fernandes Filho Liovando Marciano da Costa Leiliane Bozzi Zeferino |
| author_facet | Eliana Elizabet dos Santos Nathalie Cruz Sena Diego Balestrin Elpidio Inácio Fernandes Filho Liovando Marciano da Costa Leiliane Bozzi Zeferino |
| author_sort | Eliana Elizabet dos Santos |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T15:27:24Z |
| format | Article |
| id | doaj.art-4e8785ba875f46adb76c7f9fd2129452 |
| institution | Directory Open Access Journal |
| issn | 2179-8087 |
| language | English |
| last_indexed | 2024-12-20T15:27:24Z |
| publishDate | 2020-06-01 |
| publisher | Universidade Federal Rural do Rio de Janeiro |
| record_format | Article |
| series | Floresta e Ambiente |
| spelling | doaj.art-4e8785ba875f46adb76c7f9fd21294522022-12-21T19:35:45ZengUniversidade Federal Rural do Rio de JaneiroFloresta e Ambiente2179-80872020-06-0127310.1590/2179-8087.011518Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais StateEliana Elizabet dos Santoshttps://orcid.org/0000-0001-6904-6689Nathalie Cruz Senahttps://orcid.org/0000-0003-4118-5913Diego Balestrinhttps://orcid.org/0000-0002-4639-4231Elpidio Inácio Fernandes Filhohttps://orcid.org/0000-0003-2440-8329Liovando Marciano da Costahttps://orcid.org/0000-0001-9581-0783Leiliane Bozzi Zeferinohttps://orcid.org/0000-0003-0900-4879Abstract 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000300119&tlng=enfiresmodelingenvironmental monitoring |
| spellingShingle | Eliana Elizabet dos Santos Nathalie Cruz Sena Diego Balestrin Elpidio Inácio Fernandes Filho Liovando Marciano da Costa Leiliane Bozzi Zeferino Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State Floresta e Ambiente fires modeling environmental monitoring |
| title | Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State |
| title_full | Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State |
| title_fullStr | Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State |
| title_full_unstemmed | Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State |
| title_short | Prediction of Burned Areas Using the Random Forest Classifier in the Minas Gerais State |
| title_sort | prediction of burned areas using the random forest classifier in the minas gerais state |
| topic | fires modeling environmental monitoring |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000300119&tlng=en |
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