Open AccessFitting Volume Models for Parana Pine With a Nonlinear Regression, Genetic Algorithm and Simulated Annealing
Author(s) -
Emanuel Ari Costa,
Cristine Tagliapietra Schons,
César Augusto Guimarães Finger,
André Felipe Hess
Publication year - 2022
Publication title -
journal of agricultural science
Language(s) - English
Resource type - Journals
eISSN - 1916-9760
pISSN - 1916-9752
DOI - 10.5539/jas.v14n2p36
Subject(s) - araucaria , simulated annealing , diameter at breast height , nonlinear regression , mathematics , nonlinear system , stock (firearms) , genetic algorithm , algorithm , volume (thermodynamics) , regression analysis , forestry , regression , statistics , computer science , mathematical optimization , geography , physics , quantum mechanics , archaeology
Improving volumetric quantification of Parana pine (Araucaria angustifolia) in Mixed Ombrophilous Forest is a constant need in order to provide accurate and timely information on current and future growing stock to ensure forest management. Thus, the present study aimed to evaluate and compare the volume estimates obtained through Nonlinear Regression (NR), Genetic Algorithm (GA) and Simulated Annealing (SA) in order to generate accurate volume estimates. Volumetric equations were developed including the independent variables diameter at breast height (dbh), total height (h) and crown rate (cr) and from the fit through the NR, GA and SA approaches. The GA and SA approaches evaluated proved to be a reliable optimization strategy for parameter estimation in Parana pine volumetric modelling, however, no significant differences were found in comparison with the NR approach. This study therefore contributes through the generation of robust equations that could be used for accurate estimates of the volume of the Parana pine in southern Brazil, thus supporting the planning and establishment of management and conservation actions.