Machine Learning Methods for Woody Volume Prediction in Eucalyptus

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade Federal de Mato Grosso do Sul (UFMS)-
Autor(es): dc.contributorUniversidade Católica Dom Bosco (UCDB)-
Autor(es): dc.contributorFederal University of Fronteira do Sul (UFFS)-
Autor(es): dc.contributorState University of Mato Grosso (UNEMAT)-
Autor(es): dc.creatorSantana, Dthenifer Cordeiro-
Autor(es): dc.creatorSantos, Regimar Garcia dos-
Autor(es): dc.creatorda Silva, Pedro Henrique Neves-
Autor(es): dc.creatorPistori, Hemerson-
Autor(es): dc.creatorTeodoro, Larissa Pereira Ribeiro-
Autor(es): dc.creatorPoersch, Nerison Luis-
Autor(es): dc.creatorde Azevedo, Gileno Brito-
Autor(es): dc.creatorde Oliveira Sousa Azevedo, Glauce Taís-
Autor(es): dc.creatorda Silva Junior, Carlos Antonio-
Autor(es): dc.creatorTeodoro, Paulo Eduardo-
Data de aceite: dc.date.accessioned2025-08-21T17:29:53Z-
Data de disponibilização: dc.date.available2025-08-21T17:29:53Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/su151410968-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/307117-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/307117-
Descrição: dc.descriptionMachine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species.-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Descrição: dc.descriptionFaculty of Computing Federal University of Mato Grosso do Sul (UFMS), MS-
Descrição: dc.descriptionDepartment of Computer Engineering Universidade Católica Dom Bosco (UCDB), MS-
Descrição: dc.descriptionCampus de Chapadão do Sul Federal University of Mato Grosso do Sul (UFMS), MS-
Descrição: dc.descriptionDepartment of Agronomy Federal University of Fronteira do Sul (UFFS), RS-
Descrição: dc.descriptionDepartment of Geography State University of Mato Grosso (UNEMAT), MT-
Descrição: dc.descriptionDepartment of Agronomy State University of São Paulo (UNESP), SP-
Idioma: dc.languageen-
Relação: dc.relationSustainability (Switzerland)-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectforestry inventory-
Palavras-chave: dc.subjectshallow learner-
Palavras-chave: dc.subjecttree volume-
Título: dc.titleMachine Learning Methods for Woody Volume Prediction in Eucalyptus-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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