ARTIFICIAL NEURAL NETWORKS AND MIXED-EFFECTS MODELING TO DESCRIBE THE STEM PROFILE OF Pinus taeda L.
Journal: FLORESTA
Year: 2019

The aim of this study was to compare the effectiveness of artificial neural networks (ANNs) and mixed-effects models (MEMs) in describing the stem profile of Pinus taeda L., using sample data from 246 trees. First, three taper functions of different classes were adjusted: non-segmented, segmented, and variable-form. To adjust the models, the nonlinear regression technique (nls) was used. In the best performance equation for nls-adjusted diameter estimates, the nonlinear MEM (nlme) was applied at