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metadata.artigo.dc.title: Multilevel nonlinear mixed-effects model and machine learning for predicting the volume of Eucalyptus spp. trees
metadata.artigo.dc.creator: Dantas, Daniel
Calegario, Natalino
Acerbi Júnior, Fausto Weimar
Carvalho, Samuel de Pádua Chaves
Isaac Júnior, Marcos Antonio
Melo, Elliezer de Almeida
metadata.artigo.dc.subject: Artificial intelligence
Artificial neural network
Forest Management
Schumacher and Hall Model
Support-vector machine
Inteligência artificial
Rede neural artificial
Gestão florestal
Modelo de Schumacher e Hall
Máquina de vetores de suporte
metadata.artigo.dc.publisher: Universidade Federal de Lavras Jun-2020
metadata.artigo.dc.identifier.citation: DANTAS, D. et al. Multilevel nonlinear mixed-effects model and machine learning for predicting the volume of Eucalyptus spp. trees. Cerne, Lavras, v. 26, n. 1, p. 48-57, 2020. DOI: 10.1590/01047760202026012668.
metadata.artigo.dc.description.abstract: Volumetric equations is one of the main tools for quantifying forest stand production, and is the basis for sustainable management of forest plantations. This study aimed to assess the quality of the volumetric estimation of Eucalyptus spp. trees using a mixed-effects model, artificial neural network (ANN) and support-vector machine (SVM). The database was derived from a forest stand located in the municipalities of Bom Jardim de Minas, Lima Duarte and Arantina in Minas Gerais state, Brazil. The volume of 818 trees was accurately estimated using Smalian’s Formula. The Schumacher and Hall model was fitted by fixed-effects regression and by including multilevel random effects. The mixed model was fitted by adopting 14 different structures for the variance and covariance matrix. The best structure was selected based on the Akaike Information Criterion, Maximum Likelihood Ratio Test and Vuong’s Closeness Test. The SVM and ANN training process considered diameter at breast height and total tree height to be the independent variables. The techniques performed satisfactorily in modeling, with homogeneous distributions and low dispersion of residuals. The quality analysis criteria indicated the superior performance of the mixed model with a Huynh-Feldt structure of the variance and covariance matrix, which showed a decrease in mean relative error from 13.52% to 2.80%, whereas machine learning techniques had error values of 6.77% (SVM) and 5.81% (ANN). This study confirms that although fixed-effects models are widely used in the Brazilian forest sector, there are more effective methods for modeling dendrometric variables.
metadata.artigo.dc.language: en
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