Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

Artificial neural networks, quantile regression, and linear regression for site index prediction in the presence of outliers

Abstract: The objective of this work was to compare methods of obtaining the site index for eucalyptus (Eucalyptus spp.) stands, as well as to evaluate their impact on the stability of this index in databases with and without outliers. Three methods were tested, using linear regression, quantile reg...

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Bibliographic Details
Main Authors: Carlos Alberto Araújo Júnior, Pábulo Diogo de Souza, Adriana Leandra de Assis, Christian Dias Cabacinha, Helio Garcia Leite, Carlos Pedro Boechat Soares, Antonilmar Araújo Lopes da Silva, Renato Vinícius Oliveira Castro
Format: Article
Language:English
Published: Embrapa Informação Tecnológica 2019-05-01
Series:Pesquisa Agropecuária Brasileira
Subjects:
Eucalyptus
artificial intelligence
dominant height
forest inventory
forest modelling
non-sampling errors
Online Access:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000103200&tlng=en

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http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000103200&tlng=en

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