ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

Abstract

The present study was carried out to compare the performances of regression models and Artificial Neural Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

Influences of determined and estimated dendrometric variables on the precision of volumetric modelling

“…Artificial intelligence procedures such as artificial neural networks (ANN), machine learning, support vector machine (SVM) and random forests have recently been very frequently used in the forest sector for volumetric modelling (Özçelik et al 2017;Guera et al 2018;Silva 2020;Lima et al 2021). However, they present similar results to traditional methods for large samples (N > 200) (De Souza et al 2018).…”