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| Costa,José Janderson Ferreira; Giasson,Elvio; Silva,Elisângela Benedet da; Campos,Alcinei Ribeiro; Machado,Israel Rosa; Bonfatti,Benito Roberto; Bacic,Ivan Luiz Zilli. |
| Abstract: The objective of this work was to disaggregate the polygons of physiographic map units in order to individualize the soil classes in each one, representing them as simple soil map units and generating a more detailed soil map than the original one, making these data more useful for future reference. A physiographic map, on a 1:25,000 scale, of the Tarumãzinho watershed, located in the municipality of Águas Frias, in the state of Santa Catarina, Brazil, was used. For disaggregation, three geomorphometric parameters were applied: slope and landforms, both derived from the digital terrain model; and an elevation map. The boundaries of the physiographic units and the elevation, slope, and landform maps were subjected to cross tabulation to identify... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Decision trees; Digital soil mapping; Pedology; Soil class prediction. |
| Ano: 2019 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2019000103807 |
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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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