Feature selection by genetic algorithm in nonlinear taper model

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorLacerda, Talles Hudson Souza-
Autor(es): dc.creatorMiranda, Evandro Nunes-
Autor(es): dc.creatorLopes, Isáira Leite e-
Autor(es): dc.creatorFonseca, Guilherme Rodrigues-
Autor(es): dc.creatorFrança, Luciano Cavalcante de Jesus-
Autor(es): dc.creatorGomide, Lucas Rezende-
Data de aceite: dc.date.accessioned2026-02-09T11:27:03Z-
Data de disponibilização: dc.date.available2026-02-09T11:27:03Z-
Data de envio: dc.date.issued2022-08-18-
Data de envio: dc.date.issued2022-08-18-
Data de envio: dc.date.issued2022-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/53311-
Fonte completa do material: dc.identifierhttps://cdnsciencepub.com/doi/10.1139/cjfr-2021-0265-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1141051-
Descrição: dc.descriptionTree stem profile results from a complex structure of shapes and dimensions determined by ecological processes within the forest. However, the feature selection in the development of taper models has been underinvestigated to date. We propose a genetic algorithm (GA) to assess factors that affect the stem taper and volume of Eucalyptus urograndis trees at different ages (2, 7, and 14 years) in Brazil. A total of 213 sample trees were measured in diameter and height along the stem, crown width, crown base height, crown length, and crown ratio. These variables and the stand age were supplied to the GA that selects variables, replacing those of Kozak’s 2004 model. The performance of models was evaluated using error statistics and residual plots. The GA model was efficient in predicting diameters and volumes, mainly by increasing the accuracy of the estimates in the extreme portions of the trees. This was attributed to the selection of morphometric variables as predictors of stem taper and volume, making them understandable in ecological terms. We highlight GA as a robust tool, since it incorporated the morphometric variables in Kozak’s model that contribute to the estimates.-
Descrição: dc.descriptionLACERDA, T. H. S. et al. Feature selection by genetic algorithm in nonlinear taper model. Canadian Journal of Forest Research, [S.l.], v. 52, n. 5, p. 1-11, May 2022. DOI: 10.1139/cjfr-2021-0265.-
Idioma: dc.languageen-
Publicador: dc.publisherCanadian Science Publishing (CSP)-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceCanadian Journal of Forest Research-
Palavras-chave: dc.subjectGenetic algorithm (GA)-
Palavras-chave: dc.subjectEucalyptus urograndis-
Palavras-chave: dc.subjectKozak’s model-
Título: dc.titleFeature selection by genetic algorithm in nonlinear taper model-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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