Data/hora: 03/07/2024 13:27:07

Biblioteca(s): 	Epagri-Sede.	
Tipo da produção científica: 	Artigo em Periódico Indexado	
Circulação/Nível: 	Nacional - B	
Data corrente: 	21/11/2018	
Data da última atualização: 	21/11/2018	
Autoria: 	CAMPOS, A. R.; GIASSON, E.; COSTA, J. J. F.; MACHADO, I. R.; SILVA, E. B.; BONFATTI, B. R.	
Título: 	Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping.	
Ano de publicação: 	2018	
Fonte/Imprenta: 	Revista Brasileira de Ciência do Solo, Viçosa, v. 42, p. 1-15, 2018.	
Idioma: 	Inglês	
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.	
Palavras-Chave: 	data mining; geomorphometric variables; soil prediction.	
Categoria do assunto: 	X Pesquisa, Tecnologia e Engenharia	
URL: 	http://intranetdoc.epagri.sc.gov.br/producao_tecnico_cientifica/DOC_41595.pdf	
Registro original: 	Epagri-Sede (Epagri-Sede)	
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