Artificial neural networks in whole-stand level modeling of Eucalyptus plants

Abstract

Forestry production is traditionally predicted using mathematical modelling, where whole-stand models are prominent for providing estimates of growth and production per unit area. However, there is a need to perform research that adopts innovative tools, such as artificial Intelligence techniques. The objective of this study was to train and evaluate the efficiency of Artificial Neural Networks (ANN) in the modeling process of growth and production of Whole-Stand Level, in "equineanean" forests of the Eucalyptus genus clones. For the training of the networks, the supervised method was adopted. There were 100 networks trained, of which, for each output variable, 5 networks were selected. The criteria used to verify the quality of the training and validation of the networks were: Student's test "T", graphical analysis of the dispersion of residues, standard error of the relative estimate (Syx%), Pearson's correlation coefficient (r) between the observed and estimated values, and aggregate difference (AD). The ANN selected in the training process, which estimated the volume variables, site index and future production, when validated did not differ statistically by the Test "T" and presented adequate statistical accuracy values to the modeling process, with satisfactory correlation values (r) between the observed and estimated values, low values of standard error of the relative estimate (Syx%) and aggregate difference (AD). Artificial neural networks of the multilayer perceptron type are precise and efficient in the entire modeling process for whole-stand level of Eucalyptus plantations, therefore its use is recommended.

Artificial intelligence tools and vegetation indices combined to estimate aboveground biomass in tropical forests

“…26,68 The ANN has been applied by several studies in forest environment showing high predictive power compared to classical regression models for estimating variables of interest. 21,26,27,75,76 In addition, the ANNs and remotely sensed data combined can provide higher modeling precision in forest sites than the regression models, as they are able to assimilate a high complexity and variety of vegetation, environment, and climatic aspects. 21,24 Although developing prediction models using ANNs is a high-complexity task that requires skilled labor and high computational capacity for training them, the ANN has achieved higher estimate precisions when modeling complex relationships among different variables.…”

“…Based on the high complexity of the studied variables and the high variability commonly observed in tropical forests, we considered an error limit of ∼20% acceptable, which indicates an achievement of robust prediction results 26 , 68 . The ANN has been applied by several studies in forest environment showing high predictive power compared to classical regression models for estimating variables of interest 21 , 26 , 27 , 75 , 76 . In addition, the ANNs and remotely sensed data combined can provide higher modeling precision in forest sites than the regression models, as they are able to assimilate a high complexity and variety of vegetation, environment, and climatic aspects 21 , 24 .…”