ARTIFICIAL NEURAL NETWORKS AND MIXED-EFFECTS MODELING TO DESCRIBE THE STEM PROFILE OF Pinus taeda L.

Resumo

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 two levels, using the age class (ci) and DBH class (cd). For this, three different study scenarios were considered, with the number of coefficients with random effects ranging from one to three in each scenario. The adjustments were made using the nls and nlme functions in R software. The selected mixed-effect equations were compared with ANNs generated in Neuro 4.0 software. The taper function models and ANNs were classified according to statistical criteria and graphical analysis of residues. The tapering equation of Bi (2000) presented better performance for diameter estimates than the non-segmented and segmented equations. Application of the nlme technique in the Bi (2000) equation increased the accuracy of the diameter estimates for Pinus taeda, in relation to the adjustment using the nls technique. In the comparison of ANNs with the variations of the Bi equation of mixed-effects, the networks performed better, indicated in the description of the P. taeda profile.