Estimates of Deforestation Rates in Rural Properties in the Legal Amazon
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Floresta e Ambiente |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000200115 |
Resumo: | Abstract This study aimed to assess the potential of artificial neural networks (ANN) as a tool to estimate deforestation rates in the municipality of São Félix do Xingu, PA, Brazil. The following input variables were used: deforestation rate until 2014, slope, altitude, Euclidean distance to roads and rivers, permanent preservation area (PPA), and property area. A total of 2,800 properties were used, of which 2,000 were used for training and 800 for validation of the networks. The input layer included nine neurons: six as quantitative variables and three as categorical variables. The output layer included a single neuron - the deforestation rate. The training results indicated high correlation (r = 0.92) and root mean square error (RMSE) of 12.4%. Validation of the model estimated RMSE = 12.9% and r = 0.91. The study results evidenced the high potential of ANN as a tool to estimate farm deforestation rates. |
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Floresta e Ambiente |
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Estimates of Deforestation Rates in Rural Properties in the Legal Amazondeforestationartificial intelligencevalidationAbstract This study aimed to assess the potential of artificial neural networks (ANN) as a tool to estimate deforestation rates in the municipality of São Félix do Xingu, PA, Brazil. The following input variables were used: deforestation rate until 2014, slope, altitude, Euclidean distance to roads and rivers, permanent preservation area (PPA), and property area. A total of 2,800 properties were used, of which 2,000 were used for training and 800 for validation of the networks. The input layer included nine neurons: six as quantitative variables and three as categorical variables. The output layer included a single neuron - the deforestation rate. The training results indicated high correlation (r = 0.92) and root mean square error (RMSE) of 12.4%. Validation of the model estimated RMSE = 12.9% and r = 0.91. The study results evidenced the high potential of ANN as a tool to estimate farm deforestation rates.Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro2020-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000200115Floresta e Ambiente v.27 n.2 2020reponame:Floresta e Ambienteinstname:Universidade Federal do Rio de Janeiro (UFRJ)instacron:UFRJ10.1590/2179-8087.028317info:eu-repo/semantics/openAccessLeal,Fabrício AssisMiguel,Eder PereiraMatricardi,Eraldo Aparecido Trondolieng2020-08-05T00:00:00Zoai:scielo:S2179-80872020000200115Revistahttps://www.floram.org/PUBhttps://old.scielo.br/oai/scielo-oai.phpfloramjournal@gmail.com||floram@ufrrj.br||2179-80871415-0980opendoar:2020-08-05T00:00Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ)false |
dc.title.none.fl_str_mv |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
title |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
spellingShingle |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon Leal,Fabrício Assis deforestation artificial intelligence validation |
title_short |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
title_full |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
title_fullStr |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
title_full_unstemmed |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
title_sort |
Estimates of Deforestation Rates in Rural Properties in the Legal Amazon |
author |
Leal,Fabrício Assis |
author_facet |
Leal,Fabrício Assis Miguel,Eder Pereira Matricardi,Eraldo Aparecido Trondoli |
author_role |
author |
author2 |
Miguel,Eder Pereira Matricardi,Eraldo Aparecido Trondoli |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Leal,Fabrício Assis Miguel,Eder Pereira Matricardi,Eraldo Aparecido Trondoli |
dc.subject.por.fl_str_mv |
deforestation artificial intelligence validation |
topic |
deforestation artificial intelligence validation |
description |
Abstract This study aimed to assess the potential of artificial neural networks (ANN) as a tool to estimate deforestation rates in the municipality of São Félix do Xingu, PA, Brazil. The following input variables were used: deforestation rate until 2014, slope, altitude, Euclidean distance to roads and rivers, permanent preservation area (PPA), and property area. A total of 2,800 properties were used, of which 2,000 were used for training and 800 for validation of the networks. The input layer included nine neurons: six as quantitative variables and three as categorical variables. The output layer included a single neuron - the deforestation rate. The training results indicated high correlation (r = 0.92) and root mean square error (RMSE) of 12.4%. Validation of the model estimated RMSE = 12.9% and r = 0.91. The study results evidenced the high potential of ANN as a tool to estimate farm deforestation rates. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000200115 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872020000200115 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/2179-8087.028317 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
publisher.none.fl_str_mv |
Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro |
dc.source.none.fl_str_mv |
Floresta e Ambiente v.27 n.2 2020 reponame:Floresta e Ambiente instname:Universidade Federal do Rio de Janeiro (UFRJ) instacron:UFRJ |
instname_str |
Universidade Federal do Rio de Janeiro (UFRJ) |
instacron_str |
UFRJ |
institution |
UFRJ |
reponame_str |
Floresta e Ambiente |
collection |
Floresta e Ambiente |
repository.name.fl_str_mv |
Floresta e Ambiente - Universidade Federal do Rio de Janeiro (UFRJ) |
repository.mail.fl_str_mv |
floramjournal@gmail.com||floram@ufrrj.br|| |
_version_ |
1750128143317860352 |