Support vector machine to estimate volume of Eucalypt trees
This study aimed to test the application of the technique of support vector machines (SVM) to estimate the volume of eucalyptus trees. The data used in this study were from of 2307 trees of clonal hybrids (Eucalyptus grandis x Eucalyptus urophylla) located in southern Bahia. In the definition of str...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Brasil |
| Institución: | Universidade Federal de Viçosa (UFV) |
| Repositorio: | LOCUS Repositório Institucional da UFV |
| Idioma: | inglés |
| OAI Identifier: | oai:locus.ufv.br:123456789/16126 |
| Acceso en línea: | http://dx.doi.org/10.1590/0100-67622016000400012 http://www.locus.ufv.br/handle/123456789/16126 |
| Access Level: | acceso abierto |
| Palabra clave: | Volume equations Machines learning Schumacher and hall |
| Sumario: | This study aimed to test the application of the technique of support vector machines (SVM) to estimate the volume of eucalyptus trees. The data used in this study were from of 2307 trees of clonal hybrids (Eucalyptus grandis x Eucalyptus urophylla) located in southern Bahia. In the definition of stratification traditionally used 53 stratums were defined (defined by the stratification project and clone). He set the model of Schumacher and Hall for each stratum. The SVM were constructed to correlate the volume of trees on the basis of other independent variables which may be numeric as dbh and height and categorical as genetic material and design. The estimates were analyzed using statistical and graphical analysis of residues. The analysis consisted of the graphical inspection statistical dispersion of errors (residuals) in relation to the percentage of the values observed, and the analysis of the histogram of residues. The statistics used were the correlation between the observed and estimated volumes. The model of Schumacher and Hall showed the correlation between observed and predicted values of 0,993, and the SVM set of correlated 0,994. The SVM technology showed good adaptation to the problem, and this can use to predict the volumetric production of planted forests. |
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