Objetivou-se, neste trabalho, avaliar o ajuste do modelo volumétrico de Schumacher e Hall por diferentes algoritmos, bem como a aplicação de redes neurais artificiais para estimação do volume de madeira de eucalipto em função do diâmetro a 1,30 m do solo (DAP), da altura total (Ht) e do clone. Foram utilizadas 21 cubagens de povoamentos de clones de eucalipto com DAP variando de 4,5 a 28,3 cm e altura total de 6,6 a 33,8 m, num total de 862 árvores. O modelo volumétrico de Schumacher e Hall foi ajustado nas formas linear e não linear, com os seguintes algoritmos: Gauss-Newton, Quasi-Newton, Levenberg-Marquardt, Simplex, Hooke-Jeeves Pattern, Rosenbrock Pattern, Simplex, Hooke-Jeeves e Rosenbrock, utilizado simultaneamente com o método Quasi-Newton e com o princípio da Máxima Verossimilhança. Diferentes arquiteturas e modelos (Multilayer Perceptron – MLP e Radial Basis Function – RBF) de redes neurais artificiais foram testados, sendo selecionadas as redes que melhor representaram os dados. As estimativas dos volumes foram avaliadas por gráficos de volume estimado em função do volume observado e pelo teste estatístico L&O. Assim, conclui- se que o ajuste do modelo de Schumacher e Hall pode ser usado na sua forma linear, com boa representatividade e sem apresentar tendenciosidade; os algoritmos Gauss-Newton, Quasi-Newton e Levenberg-Marquardt mostraram- se eficientes para o ajuste do modelo volumétrico de Schumacher e Hall, e as redes neurais artificiais apresentaram boa adequação ao problema, sendo elas altamente recomendadas para realizar prognose da produção de florestas plantadas.
This research aimed at evaluating the adjustment of Schumacher and Hall volumetric model by different algorithms and the application of artificial neural networks to estimate the volume of wood of eucalyptus according to the diameter at breast height (DBH), total height (Ht) of the clone. For such, 21 scalings of stands of eucalyptus clones were used with DBH ranging from 4,5 to 28,3 cm and total height ranging from 6,6 to 33,8 m. The Schumacher and Hall volumetric model was adjusted linearly and nonlinearly with the following algorithms: Gauss-Newton, Quasi-Newton, Levenberg-Marquardt, Simplex, Hooke-Jeeves Pattern, Rosenbrock Pattern; Simplex, Hooke-Jeeves, and Rosenbrock, used simultaneously with the Quasi- Newton method and the principle of Maximum Likelihood. Different architectures and models (Multilayer Perceptron - MLP and Radial Basis Function - RBF) of artificial neural networks were tested and the networks that best represented the data were selected. Estimates of the volumes were evaluated by graphics of estimated volume according to the observed volume and by the L&O statistical test . It was concluded that the adjustment of the Schumacher and Hall model can be used in its linear form, with good representation and without presenting bias of the data; the Gauss-Newton, Quasi-Newton and Levenberg-Marquardt algorithms were effective in the adjustment of Schumacher and Hall volumetric model. The artificial neural networks showed good adequacy to the problem and are highly recommended to perform production prognoses of planted forests.
