STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS
Autor(a) principal: | |
---|---|
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/1044 |
Resumo: | The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN) to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80%) and generalization (20%). Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems. |
id |
UFLA-3_136d88e4137706cacd2c37ed006830a1 |
---|---|
oai_identifier_str |
oai:cerne.ufla.br:article/1044 |
network_acronym_str |
UFLA-3 |
network_name_str |
Cerne (Online) |
repository_id_str |
|
spelling |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKSModeling forest growth and yieldapproximation of functionsunthinned stands.The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN) to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80%) and generalization (20%). Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems.CERNECERNE2016-04-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/1044CERNE; Vol. 21 No. 1 (2015); 97-105CERNE; v. 21 n. 1 (2015); 97-1052317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/1044/815Copyright (c) 2016 CERNEinfo:eu-repo/semantics/openAccessBinoti, Mayra Luiza Marques da SilvaLeite, Helio GarciaBinoti, Daniel Henrique BredaGleriani, José Marinaldo2016-04-20T10:24:04Zoai:cerne.ufla.br:article/1044Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2016-04-20T10:24:04Cerne (Online) - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
title |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
spellingShingle |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS Binoti, Mayra Luiza Marques da Silva Modeling forest growth and yield approximation of functions unthinned stands. |
title_short |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
title_full |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
title_fullStr |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
title_full_unstemmed |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
title_sort |
STAND-LEVEL PROGNOSIS OF EUCALYPTUS CLONES USING ARTIFICIAL NEURAL NETWORKS |
author |
Binoti, Mayra Luiza Marques da Silva |
author_facet |
Binoti, Mayra Luiza Marques da Silva Leite, Helio Garcia Binoti, Daniel Henrique Breda Gleriani, José Marinaldo |
author_role |
author |
author2 |
Leite, Helio Garcia Binoti, Daniel Henrique Breda Gleriani, José Marinaldo |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Binoti, Mayra Luiza Marques da Silva Leite, Helio Garcia Binoti, Daniel Henrique Breda Gleriani, José Marinaldo |
dc.subject.por.fl_str_mv |
Modeling forest growth and yield approximation of functions unthinned stands. |
topic |
Modeling forest growth and yield approximation of functions unthinned stands. |
description |
The objective of this study was to train, implement and evaluate the efficiency of artificial neural networks (ANN) to perform production prognosis of even-aged stands of eucalyptus clones. The data used were from plantations located in southern Bahia, totaling about 2,000 acres of forest. Numeric variables, such as age, basal area, volume and categorical variables, such as soil class texture, spacing, land relief, project and clone were used. The data were randomly divided into two groups: training (80%) and generalization (20%). Three types of networks were trained: perceptron, multilayer perceptron networks and radial basis function. The RNA that showed the best performance in training and generalization were selected to perform the prognosis with data from the first forest inventory. We conclude that the RNA had satisfactory results, showing the potential and applicability of the technique in solving measurement and forest management problems. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-07 |
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/1044 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/1044 |
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/1044/815 |
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. 21 No. 1 (2015); 97-105 CERNE; v. 21 n. 1 (2015); 97-105 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_ |
1789440344354455552 |