ABSTRACT
The understanding of the relationship between the properties of wood and charcoal makes it possible to improve the production of charcoal. Therefore, the random forest algorithm was used in this study to analyze the influence of eucalyptus wood properties on the quality of charcoal as well as the accuracy of the predicted values concerning the results estimated by support vector regression and multiple linear regression. Six properties of wood and six properties of charcoal obtained from the hybrid Eucalyptus grandis x Eucalyptus urophylla and from twelve clones of Corymbia torelliana x Corymbia critriodora at the age of seven were measured. In the analysis, the measure of mean decrease in node impurity (residual sum of squares) calculated with the random forest and the copula correlation was used to evaluate the relationship between properties of wood and charcoal. The random forest was compared to the support vector regression and multiple linear regression through the coefficient of determination, linear correlation between observed and predicted values, mean absolute error and root mean squared error. The accuracy of the random forest was greater than that obtained with the support vector regression and multiple linear regression, mainly in terms of the coefficient of determination and the linear correlation between observed and predicted values. The yield and quality of the charcoal produced from clones were mainly influenced by the holocellulose content, heartwood/sapwood ratio, and basic wood density. The apparent relative density of charcoal was the variable in which the random forest algorithm reached the best level of explanation of the variability as a function of the properties of wood, while the minor error was observed for the fixed carbon content.
Keywords:
Supervised learning; Corymbia; Random forest
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