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

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DOI:

https://doi.org/10.5380/rf.v54i1.83115

Palavras-chave:

computational modeling, estuarine native forests, volumetry.

Resumo

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.

Biografia do Autor

Anthoinny Vittória dos Santos Silva, Universidade do Estado do Amapá (UEAP), Laboratório Manejo Florestal, Macapá, Amapá

Acadêmica de Engenharia Florestal na Universidade do Estado do Amapá (UEAP), bolsista de iniciação cientifica (CNPQ/UEAP) na área de conhecimento Ciência Agrárias, na grande área Recursos Florestais e Engenharia florestal, com ênfase em Manejo Florestal, com a linha de pesquisa em Dendrometria e Inventário Florestal. Atualmente com trabalhos voltado para aprendizado de máquina (redes neurais artificiais e máquina de vetor de suporte), modelos matemáticos lineares e não lineares aplicado as ciências florestais.

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Publicado

06-05-2024

Como Citar

dos Santos Silva, A. V., Teixeira de Souza, R. G., Figueiredo de Souza, R. L., Mendes Santos, R., Borges de Lima, R., & Coelho de Abreu, J. (2024). USE OF MACHINE LEARNING, FIXED AND MIXED MODELS FOR VOLUME ESTIMATION IN FLOODPLAIN FOREST IN THE AMAZON ESTUARY. FLORESTA, 54(1). https://doi.org/10.5380/rf.v54i1.83115

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