Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus

Using advanced machine learning techniques as an alternative to conventional double-entry volume equations, a regression model of the inside-bark volume (dependent variable) for standing Eucalyptus globulus trunks (or main stems) has been built as a function of the following three independent variab...

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Veröffentlicht in:Applied mathematical modelling 2012-12, Vol.36 (12), p.6137-6145
Hauptverfasser: Garcia Nieto, PJ, Martinez Torres, J, Araujo Fernandez, M, Ordonez Galan, C
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Sprache:eng
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Zusammenfassung:Using advanced machine learning techniques as an alternative to conventional double-entry volume equations, a regression model of the inside-bark volume (dependent variable) for standing Eucalyptus globulus trunks (or main stems) has been built as a function of the following three independent variables: age, height and outside-bark diameter at breast height (DBH). The experimental observed data (age, height, outside-bark DBH and inside-bark volume) for 142 trees (E. globulus) were measured and a nonlinear model was built using a data-mining methodology based on support vector machines (SVM) and multilayer perceptron networks (MLP) for regression problems. Coefficients of determination and Furnival’s indices indicate the superiority of the SVM with a radial kernel over the allometric regression models and the MLP.
ISSN:0307-904X
DOI:10.1016/j.apm.2012.02.016