Artificial intelligence tools in predicting the volume of trees within a forest stand

Abstract

The goal of this study was to train, validate, select and evaluate artificial neural networks (ANN) to predict the individual volume of wood in eucalyptus stand, based on the diameter at breast height (DBH) and DBH with the total height (Ht). Data was obtained from a plantation of Eucalyptus urophylla ST Blake of seven years of age, located in the state of Goiás, Brazil. Sixteen plots were randomly set in this area, from which the variables diameter, total height and volume were accounted. The volume of all the trees in each plot was measured by the Smalian method; afterwards, the data were checked for normality using the Shapiro-Wilk test. Sequentially, perceptron network settings (ANN1 = DBH and Ht; and ANN2 = DBH) were trained using sigmoid activation functions and resilient propagation (Rprop) algorithm. In addition, a root-mean-square error (RMSE) of less than 1% was adopted as stopping criterion or when this rose again. The selected ANNs presented low variation among the task-specific training indices, selection and evaluation, showing correlation ( ̂) between predicted and observed volume (0.9945 and 0.9898), and RMSE from 1.75 and 2.22%, respectively. The Shapiro-Wilk test highlighted non-normality of data distribution; hence, various selected ANNs were subjected to the Kruskal-Wallis test for validation, as well as for comparison with each other and sequentially submitted to the overall group difference test. The test demonstrated that both ANNs were able to predict tree volume; leading to the conclusion that multilayer perceptron neural networks (MLPNNs), using just one neuron input- the diameter, are as precise and accurate as networks using two neuronsthe diameter and height, in order to predict individual volume of E. urophylla