Fitting Volume Models for Parana Pine With a Nonlinear Regression, Genetic Algorithm and Simulated Annealing

Improving volumetric quantification of Parana pine (Araucaria angustifolia) in Mixed Ombrophilous Forest is a constant need order to provide accurate and timely information on current future growing stock ensure forest management. Thus, the present study aimed evaluate compare volume estimates obtained through Nonlinear Regression (NR), Genetic Algorithm (GA) Simulated Annealing (SA) generate estimates. Volumetric equations were developed including independent variables diameter at breast height (dbh), total (h) crown rate (cr) from fit NR, GA SA approaches. The approaches evaluated proved be reliable optimization strategy for parameter estimation modelling, however, no significant differences found comparison with NR approach. This therefore contributes generation robust that could used southern Brazil, thus supporting planning establishment management conservation actions.