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 Universidade Federal de Minas Gerais
  • Daniel Henrique Breda Binoti Universidade Federal de Viçosa
  • Mayra Luiza Marques da Silva Binoti Universidade Federal do Espírito Santo
  • Hélio Garcia Leite Universidade Federal de 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, Universidade Federal de Minas Gerais

http://lattes.cnpq.br/1043806764448100

Daniel Henrique Breda Binoti, Universidade Federal de Viçosa

http://lattes.cnpq.br/2540451543790663

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Published

2015-09-30

How to Cite

CORDEIRO, Márcio Assis; PEREIRA, Nayara Natacha de Jesus; BINOTI, Daniel Henrique Breda; BINOTI, Mayra Luiza Marques da Silva; LEITE, Hélio Garcia. Estimate of Acacia mangium volume using techniques of artificial neural networks and support vector machines. Pesquisa Florestal Brasileira, [S. l.], v. 35, n. 83, p. 255–261, 2015. DOI: 10.4336/2015.pfb.35.83.596. Disponível em: https://pfb.sede.embrapa.br/pfb/article/view/596. Acesso em: 19 apr. 2025.

Issue

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

Section

Articles

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