Artificial neural networks and regression analysis for volume estimation in native species

Detalhes bibliográficos
Autor(a) principal: Amorim, Lucas M.
Data de Publicação: 2021
Outros Autores: Leite, Elton da S., Souza, Deoclides R. de, Silva, Liniker F. da, Mello, Carlos R. de, Lima, José M. de
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/49340
Resumo: Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.
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spelling Artificial neural networks and regression analysis for volume estimation in native speciesRedes neurais artificiais e análise de regressão para estimativa de volume de espécies nativasNative forestProduction volumePrediction modelsArtificial neural networks (ANNs)Florestas nativasVolume de produçãoModelos de prediçãoRedes neurais artificiais (RNAs)Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.O uso de modelos para estimar a produção florestal é uma importante ferramenta em áreas plantadas. Embora esse assunto tenha sido estudado em todo o mundo, ainda falta conhecimento a respeito da medição de volume para locais específicos, como os do Nordeste do Brasil. Desta forma, objetivou-se com este estudo avaliar o potencial de predição de redes neurais artificiais e regressão para a estimativa do volume de madeira em povoamentos homogêneos de Anadantera macrocarpa, Genipa americana e Mimosa caesalpiniflolia. Os métodos de regressão e de redes neurais artificiais (RNAs) mostraram-se aplicáveis para a estimativa do volume individual dos povoamentos em diferentes espaçamentos, aos sete anos de idade. O modelo de regressão de Spurr apresentou melhores resultados estatísticos e dispersão dos erros não tendenciosos para as espécies Anadantera macrocarpa e Genipa americana. Já o modelo de Shumacher-Hall foi mais preciso para a estimativa do volume da espécie Mimosa caesalpinifolia. As RNAs, com dois neurônios na camada intermediária, proporcionaram melhores ajustes para as três espécies, portanto, são recomendadas para estimar os volumes individuais das espécies avaliadas, por mostrar maior precisão, em relação à regressão, na estimativa do volume das espécies nativas avaliadas.Universidade Federal de Campina Grande2022-02-15T22:00:20Z2022-02-15T22:00:20Z2021-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfAMORIM, L. M. et al. Artificial neural networks and regression analysis for volume estimation in native species. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 25, n. 10, p. 664-669, Oct. 2021. DOI: https://doi.org/10.1590/1807-1929/agriambi.v25n10p664-669 .http://repositorio.ufla.br/jspui/handle/1/49340Revista Brasileira de Engenharia Agrícola e Ambiental - Brazilian Journal of Agricultural and Environmental Engineeringreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAmorim, Lucas M.Leite, Elton da S.Souza, Deoclides R. deSilva, Liniker F. daMello, Carlos R. deLima, José M. deeng2022-02-15T22:00:46Zoai:localhost:1/49340Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2022-02-15T22:00:46Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Artificial neural networks and regression analysis for volume estimation in native species
Redes neurais artificiais e análise de regressão para estimativa de volume de espécies nativas
title Artificial neural networks and regression analysis for volume estimation in native species
spellingShingle Artificial neural networks and regression analysis for volume estimation in native species
Amorim, Lucas M.
Native forest
Production volume
Prediction models
Artificial neural networks (ANNs)
Florestas nativas
Volume de produção
Modelos de predição
Redes neurais artificiais (RNAs)
title_short Artificial neural networks and regression analysis for volume estimation in native species
title_full Artificial neural networks and regression analysis for volume estimation in native species
title_fullStr Artificial neural networks and regression analysis for volume estimation in native species
title_full_unstemmed Artificial neural networks and regression analysis for volume estimation in native species
title_sort Artificial neural networks and regression analysis for volume estimation in native species
author Amorim, Lucas M.
author_facet Amorim, Lucas M.
Leite, Elton da S.
Souza, Deoclides R. de
Silva, Liniker F. da
Mello, Carlos R. de
Lima, José M. de
author_role author
author2 Leite, Elton da S.
Souza, Deoclides R. de
Silva, Liniker F. da
Mello, Carlos R. de
Lima, José M. de
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Amorim, Lucas M.
Leite, Elton da S.
Souza, Deoclides R. de
Silva, Liniker F. da
Mello, Carlos R. de
Lima, José M. de
dc.subject.por.fl_str_mv Native forest
Production volume
Prediction models
Artificial neural networks (ANNs)
Florestas nativas
Volume de produção
Modelos de predição
Redes neurais artificiais (RNAs)
topic Native forest
Production volume
Prediction models
Artificial neural networks (ANNs)
Florestas nativas
Volume de produção
Modelos de predição
Redes neurais artificiais (RNAs)
description Modeling is an important tool to estimate forest production in planted areas. Although this issue has been studied worldwide, knowledge regarding volume measurement in specific locations such as Northeast Brazil is still scarce. The present study aimed to evaluated the effectiveness of artificial neural networks (ANNs) and regression analysis in estimating the timber volume of homogeneous stands of Anadantera macrocarpa, Genipa americana, and Mimosa casalpinifolia, in order to better predict the growth and production of these species. Both methods were suitable for estimating the individual volume in 7-year-old stands with different spacing. The Spurr regression model showed better statistical results and dispersion of unbiased errors for Anadantera macrocarpa and Genipa americana, whereas the Shumacher-Hall model provided more accurate volume estimates for Mimosa caesalpinifolia. The ANNs calibrated with two neurons in the middle layer exhibited the best fit for all three species. As such, artificial neural networks can be recommended to estimate the individual volumes of the species analyzed in the study area.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
2022-02-15T22:00:20Z
2022-02-15T22:00:20Z
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 AMORIM, L. M. et al. Artificial neural networks and regression analysis for volume estimation in native species. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 25, n. 10, p. 664-669, Oct. 2021. DOI: https://doi.org/10.1590/1807-1929/agriambi.v25n10p664-669 .
http://repositorio.ufla.br/jspui/handle/1/49340
identifier_str_mv AMORIM, L. M. et al. Artificial neural networks and regression analysis for volume estimation in native species. Revista Brasileira de Engenharia Agrícola e Ambiental, Campina Grande, v. 25, n. 10, p. 664-669, Oct. 2021. DOI: https://doi.org/10.1590/1807-1929/agriambi.v25n10p664-669 .
url http://repositorio.ufla.br/jspui/handle/1/49340
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal de Campina Grande
publisher.none.fl_str_mv Universidade Federal de Campina Grande
dc.source.none.fl_str_mv Revista Brasileira de Engenharia Agrícola e Ambiental - Brazilian Journal of Agricultural and Environmental Engineering
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
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