AN APPROACH TO DIAMETER DISTRIBUTION MODELING USING CELLULAR AUTOMATA AND ARTIFICIAL NEURAL NETWORK
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , |
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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oai:cerne.ufla.br:article/951 |
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Cerne (Online) |
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|
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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 |
_version_ |
1799874942322343936 |