USE OF MACHINE LEARNING, FIXED AND MIXED MODELS FOR VOLUME ESTIMATION IN FLOODPLAIN FOREST IN THE AMAZON ESTUARY

Abstract

The present study aimed to evaluate the performance of artificial neural networks (ANNs), support vector machines (SVM), and fixed and mixed models to estimate the volume of individual trees in an Amazonian estuary floodplain forest. The forest inventory was conducted in an area located in the district of Itatupã - Gurupá, Pará, Brazil. The Schumacher and Hall linear model was used in its fixed and mixed form to estimate the volume. A total of 240 ANNs and 32 SVM configurations were trained, with 4 variations of the input variables: diameter at breast height (D), commercial height (hm), diameter classes (CCD), commercial height classes (CChm),) and species (SP), with the volume (V) being the output variable. The ANNs were trained in the Neuro 4.0.6 software program, and the analyses of the fixed and mixed regression models and SVM were performed in the R software program. The AIC (Akaike’s Information Criterion) and BIC (Bayesian Information Criterion) information criteria were used for the regression models, while the following were used for comparisons of all models: correlation coefficient (rYŶ), bias, root mean squared error (RMSE), and residual distribution analysis. The best statistical metrics were obtained by the ANN of the RPROP- algorithm with D+hm+CCD+CChm inputs, with eight neurons in the hidden layer and hyperbolic tangent activation function, with 0.9827 of rYŶ RMSE of 0.2439, bias of 0.0080 and residual graphic distribution tending to homogeneity.