AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK

Detalhes bibliográficos
Autor(a) principal: Binoti, Daniel Henrique Breda
Data de Publicação: 2016
Outros Autores: Binoti, Mayra Luiza Marques da Silva, Leite, Helio Garcia, Silva, Antonilmar Araújo Lopes da, Albuquerque, Ana Carolina
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Cerne (Online)
Texto Completo: https://cerne.ufla.br/site/index.php/CERNE/article/view/951
Resumo: This study presents a diametric distribution model based on a one-dimensional cellular automata model (CA) and artificial neural network (ANN). Each cell of CA represents a dbh class, with the future state predicted in function of the present state of this cell, of the four neighboring cells and of its present and future age. An ANN was used as rule of evolution. Accuracy was evaluated by applying: statistical procedure proposed by Leite and Oliveira (2002); relation between observed and estimated frequency; and biological realism of the built model. Of the trained networks, were selected the ten representing the evolution of the diameter distribution with greater accuracy. Among these ten ANN, seven had estimated values statistically equal to observed (p>0.01). The proposed modeling approach estimates accurately future diameter distributions.
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spelling AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORKArtificial intelligencediameter distributioneucalyptus.This study presents a diametric distribution model based on a one-dimensional cellular automata model (CA) and artificial neural network (ANN). Each cell of CA represents a dbh class, with the future state predicted in function of the present state of this cell, of the four neighboring cells and of its present and future age. An ANN was used as rule of evolution. Accuracy was evaluated by applying: statistical procedure proposed by Leite and Oliveira (2002); relation between observed and estimated frequency; and biological realism of the built model. Of the trained networks, were selected the ten representing the evolution of the diameter distribution with greater accuracy. Among these ten ANN, seven had estimated values statistically equal to observed (p>0.01). The proposed modeling approach estimates accurately future diameter distributions.CERNECERNE2016-04-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/951CERNE; Vol. 19 No. 4 (2013); 677-685CERNE; v. 19 n. 4 (2013); 677-6852317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/951/725Copyright (c) 2016 CERNEinfo:eu-repo/semantics/openAccessBinoti, Daniel Henrique BredaBinoti, Mayra Luiza Marques da SilvaLeite, Helio GarciaSilva, Antonilmar Araújo Lopes daAlbuquerque, Ana Carolina2016-04-06T13:20:59Zoai:cerne.ufla.br:article/951Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:14.039279Cerne (Online) - Universidade Federal de Lavras (UFLA)true
dc.title.none.fl_str_mv AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
title AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
spellingShingle AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
Binoti, Daniel Henrique Breda
Artificial intelligence
diameter distribution
eucalyptus.
title_short AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
title_full AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
title_fullStr AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
title_full_unstemmed AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
title_sort AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
author Binoti, Daniel Henrique Breda
author_facet Binoti, Daniel Henrique Breda
Binoti, Mayra Luiza Marques da Silva
Leite, Helio Garcia
Silva, Antonilmar Araújo Lopes da
Albuquerque, Ana Carolina
author_role author
author2 Binoti, Mayra Luiza Marques da Silva
Leite, Helio Garcia
Silva, Antonilmar Araújo Lopes da
Albuquerque, Ana Carolina
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Binoti, Daniel Henrique Breda
Binoti, Mayra Luiza Marques da Silva
Leite, Helio Garcia
Silva, Antonilmar Araújo Lopes da
Albuquerque, Ana Carolina
dc.subject.por.fl_str_mv Artificial intelligence
diameter distribution
eucalyptus.
topic Artificial intelligence
diameter distribution
eucalyptus.
description This study presents a diametric distribution model based on a one-dimensional cellular automata model (CA) and artificial neural network (ANN). Each cell of CA represents a dbh class, with the future state predicted in function of the present state of this cell, of the four neighboring cells and of its present and future age. An ANN was used as rule of evolution. Accuracy was evaluated by applying: statistical procedure proposed by Leite and Oliveira (2002); relation between observed and estimated frequency; and biological realism of the built model. Of the trained networks, were selected the ten representing the evolution of the diameter distribution with greater accuracy. Among these ten ANN, seven had estimated values statistically equal to observed (p>0.01). The proposed modeling approach estimates accurately future diameter distributions.
publishDate 2016
dc.date.none.fl_str_mv 2016-04-06
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://cerne.ufla.br/site/index.php/CERNE/article/view/951
url https://cerne.ufla.br/site/index.php/CERNE/article/view/951
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://cerne.ufla.br/site/index.php/CERNE/article/view/951/725
dc.rights.driver.fl_str_mv Copyright (c) 2016 CERNE
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 CERNE
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv CERNE
CERNE
publisher.none.fl_str_mv CERNE
CERNE
dc.source.none.fl_str_mv CERNE; Vol. 19 No. 4 (2013); 677-685
CERNE; v. 19 n. 4 (2013); 677-685
2317-6342
0104-7760
reponame:Cerne (Online)
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Cerne (Online)
collection Cerne (Online)
repository.name.fl_str_mv Cerne (Online) - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv cerne@dcf.ufla.br||cerne@dcf.ufla.br
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