Estimativa do volume de Acacia mangium utilizando técnicas de redes neurais artificiais e máquinas vetor de suporte

Márcio Assis Cordeiro; Nayara Natacha de Jesus Pereira; Daniel Henrique Breda Binoti; Mayra Luiza Marques da Silva Binoti; Hélio Garcia Leite

doi:10.4336/2015.pfb.35.83.596

Estimate of Acacia mangium volume using techniques of artificial neural networks and support vector machines

Authors

Márcio Assis Cordeiro Amcel Floresta e Celulose
Nayara Natacha de Jesus Pereira Federal University of Minas Gerais
Daniel Henrique Breda Binoti Federal University of Viçosa
Mayra Luiza Marques da Silva Binoti Federal University of Espírito Santo
Hélio Garcia Leite Federal University of Viçosa

DOI:

https://doi.org/10.4336/2015.pfb.35.83.596

Keywords:

Smalian, Modeling, Volumetric estimates

Abstract

The present study aimed to show the results of Acacia mangium volumetric estimates obtained through the Schumacher and Hall model compared to the methods of artificial neural networks and support vector machines. To enable this comparative analysis, we used data from 31 trees of Acacia mangium aged 14-17, from a stand located in the northern region of the state of Amapá. Diameter and bark thickness of the trees were measured into relative heights along the stem into 14 sections (0.05%, 1%, 5%, 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 95%), with measurement. Total volume with bark was obtained by applying the Smalian formula. In general, the methods that differ from traditional methods showed statistically superior results.

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Author Biographies

Márcio Assis Cordeiro, Amcel Floresta e Celulose

http://lattes.cnpq.br/8250679741372197
Nayara Natacha de Jesus Pereira, Federal University of Minas Gerais

http://lattes.cnpq.br/1043806764448100
Daniel Henrique Breda Binoti, Federal University of Viçosa

http://lattes.cnpq.br/2540451543790663

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Published

2015-09-30

Issue

Vol. 35 No. 83 (2015): jul./set.

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How to Cite

CORDEIRO, M. A. et al. Estimate of Acacia mangium volume using techniques of artificial neural networks and support vector machines. Pesquisa Florestal Brasileira, v. 35, n. 83, p. 255–261, 30 Sep.2015.

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Estimate of Acacia mangium volume using techniques of artificial neural networks and support vector machines

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