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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 |
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Menezes,Michele Duarte de; Silva,Sérgio Henrique Godinho; Owens,Phillip Ray; Curi,Nilton. |
In Brazil, soil surveys in more detailed scale are still scarce and necessary to more adequately support the decision makers for planning soil and environment activities in small areas. Hence, this review addresses some digital soil mapping techniques that enable faster production of soil surveys, beyond fitting continuous spatial distribution of soil properties into discrete soil categories, in accordance with the inherent complexity of soil variation, increasing the accuracy of spatial information. The technique focused here is knowledge-based in expert systems, under fuzzy logic and vector of similarity. For that, a contextualization of each tool in the soil types and properties prediction is provided, as well as some options of knowledge extraction... |
Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Digital soil mapping; Soil prediction; Conditioned Latin hypercube sampling; Knowledge miner. |
Ano: 2013 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542013000400001 |
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