Artificial neural network to estimate the basic density of cerrado wood
DOI:
https://doi.org/10.4336/2018.pfb.38e201801656Keywords:
Artificial intelligence, Wood density, PilodynAbstract
The basic density of wood is an important property because it is related to the final product in the various uses that wood has. However, its determination demands time and costs, which justifies the use of more refined techniques for its estimation, such as artificial neural networks (ANN). The objective was to evaluate the use of artificial neural networks to estimate the basic density of species of cerrado stricto sensu with the use of Pilodyn and dendrometric variables. To compare the results obtained by ANN, regression models were adjusted. The best performing neural network was the one that used as input variables the depth of penetration (Pilodyn), species and DAP, presenting R² values of 0.72 and with root mean square error in percentage (RMSE%) of 5.69. The regression model presented R² value of 0.72 and RMSE% of 9.19. The artificial neural networks can estimate the basic wood density of species of cerrado stricto sensu studied in this study with satisfactory results.Downloads
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Published
2018-12-29
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How to Cite
SILVA, J. P. M. et al. Artificial neural network to estimate the basic density of cerrado wood. Pesquisa Florestal Brasileira, v. 38, 29 Dec.2018.
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