|
|
| Campos,Alcinei Ribeiro; Giasson,Elvio; Costa,José Janderson Ferreira; Machado,Israel Rosa; Silva,Elisângela Benedet da; Bonfatti,Benito Roberto. |
| ABSTRACT 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... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Data mining; Geomorphometric variables; Soil prediction. |
| Ano: 2018 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100315 |
| |
