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| Registros recuperados : 297 | |
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Registro Completo
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Biblioteca(s): |
Epagri-Sede. |
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Data corrente: |
21/11/2018 |
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Data da última atualização: |
21/11/2018 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
Nacional - B |
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Autoria: |
CAMPOS, A. R.; GIASSON, E.; COSTA, J. J. F.; MACHADO, I. R.; SILVA, E. B.; BONFATTI, B. R. |
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Título: |
Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Revista Brasileira de Ciência do Solo, Viçosa, v. 42, p. 1-15, 2018. |
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Idioma: |
Inglês |
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Conteúdo: |
A large number of predictor variables can be used in digital soil mapping;
however, the presence of irrelevant covariables may compromise the prediction of
soil types. Thus, algorithms can be applied to select the most relevant predictors. This
study aimed to compare three covariable selection systems (two filter algorithms and
one wrapper algorithm) and assess their impacts on the predictive model. The study
area was the Lajeado River Watershed in the state of Rio Grande do Sul, Brazil. We used
forty predictor covariables, derived from a digital elevation model with 30 m resolution,
in which the three selection models were applied and separated into subsets. These
subsets were used to assess performance by applying four prediction algorithms. The
wrapper method obtained the best performance values for the predictive model in all
the algorithms evaluated. The three selection methods applied reduced the number
of covariables in the predictive models by 70 % and enabled prediction of the 14 soil
mapping units. |
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Palavras-Chave: |
data mining; geomorphometric variables; soil prediction. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01700naa a2200217 a 4500
001 1127957
005 2018-11-21
008 2018 bl uuuu u00u1 u #d
100 1 $aCAMPOS, A. R.
245 $aSelection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping.$h[electronic resource]
260 $c2018
520 $aA large number of predictor variables can be used in digital soil mapping; however, the presence of irrelevant covariables may compromise the prediction of soil types. Thus, algorithms can be applied to select the most relevant predictors. This study aimed to compare three covariable selection systems (two filter algorithms and one wrapper algorithm) and assess their impacts on the predictive model. The study area was the Lajeado River Watershed in the state of Rio Grande do Sul, Brazil. We used forty predictor covariables, derived from a digital elevation model with 30 m resolution, in which the three selection models were applied and separated into subsets. These subsets were used to assess performance by applying four prediction algorithms. The wrapper method obtained the best performance values for the predictive model in all the algorithms evaluated. The three selection methods applied reduced the number of covariables in the predictive models by 70 % and enabled prediction of the 14 soil mapping units.
653 $adata mining
653 $ageomorphometric variables
653 $asoil prediction
700 1 $aGIASSON, E.
700 1 $aCOSTA, J. J. F.
700 1 $aMACHADO, I. R.
700 1 $aSILVA, E. B.
700 1 $aBONFATTI, B. R.
773 $tRevista Brasileira de Ciência do Solo, Viçosa$gv. 42, p. 1-15, 2018.
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