Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | https://doi.org/10.1590/S1678-3921.pab2019.v54.00078 http://hdl.handle.net/1843/43254 https://orcid.org/0000-0002-8148-083X https://orcid.org/0000-0003-0909-8633 |
Resumo: | The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used. |
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2022-07-14T11:43:54Z2022-07-14T11:43:54Z20195418https://doi.org/10.1590/S1678-3921.pab2019.v54.000781678-3921http://hdl.handle.net/1843/43254https://orcid.org/0000-0002-8148-083Xhttps://orcid.org/0000-0003-0909-8633The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used.O objetivo deste trabalho foi comparar métodos para obtenção do índice de sítio para povoamentos de eucalipto (Eucalyptus spp.), bem como avaliar seus impactos na estabilidade desse índice em bases de dados com e sem a presença de “outliers”. Foram testados três métodos, com uso de regressão linear, regressão quantílica e rede neural artificial. Foram utilizadas 22 parcelas permanentes de inventário florestal contínuo, medidas em árvores com idade de 23 a 83 meses. Os outliers foram identificados com uso de gráfico de boxplot. A rede neural artificial proporcionou melhores resultados que as regressões linear e quantílica, tanto para as estimativas de altura dominante quanto do índice de sítio. A estabilidade da classificação do índice de sítio obtida pela rede neural artificial também foi melhor que a obtida com os outros métodos, independentemente da presença ou da ausência de outliers na base de dados. Isso indica que a rede neural artificial é uma técnica sólida de modelagem na presença de outliers. Quando a causa da presença de outliers na base de dados não é conhecida, eles podem ser mantidos nela se técnicas como as de redes neurais artificiais ou de regressão quantílica forem utilizadas.engUniversidade Federal de Minas GeraisUFMGBrasilICA - INSTITUTO DE CIÊNCIAS AGRÁRIASPesquisa Agropecuária BrasileiraEucaliptoInteligência artificialLevantamentos florestaisArtificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliersRedes neurais artificiais, regressão quantílica e regressão linear para predição do índice de sítio na presença de “outliers”info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttps://www.scielo.br/j/pab/a/XMPzVhnkhZzSGnDckwWfFCb/?format=pdfCarlos Alberto Araújo JúniorPábulo Diogo de SouzaAdriana Leandra de AssisChristian Dias CabacinhaHelio Garcia LeiteCarlos Pedro Boechat SoaresAntonilmar Araújo Lopes da SilvaRenato Vinícius Oliveira Castroinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALArtificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers.pdfArtificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers.pdfapplication/pdf636338https://repositorio.ufmg.br/bitstream/1843/43254/2/Artificial%20neural%20networks%2c%20quantile%20regression%2c%20and%20linear%20regression%20for%20site%20index%20prediction%20in%20the%20presence%20of%20outliers.pdf9e92c519590427ca63369b402d769c83MD52LICENSELicense.txtLicense.txttext/plain; charset=utf-82042https://repositorio.ufmg.br/bitstream/1843/43254/1/License.txtfa505098d172de0bc8864fc1287ffe22MD511843/432542022-07-14 08:43:54.396oai:repositorio.ufmg.br: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Repositório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-07-14T11:43:54Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
dc.title.alternative.pt_BR.fl_str_mv |
Redes neurais artificiais, regressão quantílica e regressão linear para predição do índice de sítio na presença de “outliers” |
title |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
spellingShingle |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers Carlos Alberto Araújo Júnior Eucalipto Inteligência artificial Levantamentos florestais |
title_short |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
title_full |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
title_fullStr |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
title_full_unstemmed |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
title_sort |
Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers |
author |
Carlos Alberto Araújo Júnior |
author_facet |
Carlos Alberto Araújo Júnior Pábulo Diogo de Souza Adriana Leandra de Assis Christian Dias Cabacinha Helio Garcia Leite Carlos Pedro Boechat Soares Antonilmar Araújo Lopes da Silva Renato Vinícius Oliveira Castro |
author_role |
author |
author2 |
Pábulo Diogo de Souza Adriana Leandra de Assis Christian Dias Cabacinha Helio Garcia Leite Carlos Pedro Boechat Soares Antonilmar Araújo Lopes da Silva Renato Vinícius Oliveira Castro |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Carlos Alberto Araújo Júnior Pábulo Diogo de Souza Adriana Leandra de Assis Christian Dias Cabacinha Helio Garcia Leite Carlos Pedro Boechat Soares Antonilmar Araújo Lopes da Silva Renato Vinícius Oliveira Castro |
dc.subject.other.pt_BR.fl_str_mv |
Eucalipto Inteligência artificial Levantamentos florestais |
topic |
Eucalipto Inteligência artificial Levantamentos florestais |
description |
The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile regression, and artificial neural network. Twenty-two permanent plots from a continuous forest inventory were used, measured in trees with ages from 23 to 83 months. The outliers were identified using a boxplot graphic. The artificial neural network showed better results than the linear and quantile regressions, both for dominant height and site index estimates. The stability obtained for the site index classification by the artificial neural network was also better than the one obtained by the other methods, regardless of the presence or the absence of outliers in the database. This shows that the artificial neural network is a solid modelling technique in the presence of outliers. When the cause of the presence of outliers in the database is not known, they can be kept in it if techniques as artificial neural networks or quantile regression are used. |
publishDate |
2019 |
dc.date.issued.fl_str_mv |
2019 |
dc.date.accessioned.fl_str_mv |
2022-07-14T11:43:54Z |
dc.date.available.fl_str_mv |
2022-07-14T11:43:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/43254 |
dc.identifier.doi.pt_BR.fl_str_mv |
https://doi.org/10.1590/S1678-3921.pab2019.v54.00078 |
dc.identifier.issn.pt_BR.fl_str_mv |
1678-3921 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0002-8148-083X https://orcid.org/0000-0003-0909-8633 |
url |
https://doi.org/10.1590/S1678-3921.pab2019.v54.00078 http://hdl.handle.net/1843/43254 https://orcid.org/0000-0002-8148-083X https://orcid.org/0000-0003-0909-8633 |
identifier_str_mv |
1678-3921 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Pesquisa Agropecuária Brasileira |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
ICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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Universidade Federal de Minas Gerais (UFMG) |
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UFMG |
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UFMG |
reponame_str |
Repositório Institucional da UFMG |
collection |
Repositório Institucional da UFMG |
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