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| Batista,Pedro Velloso Gomes; Silva,Marx Leandro Naves; Avalos,Fabio Arnaldo Pomar; Oliveira,Marcelo Silva de; Menezes,Michele Duarte de; Curi,Nilton. |
| ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Geostatistics; Spatial prediction; Regression kriging; Riverbed morphology. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542017000400402 |
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| Silva,Sérgio Henrique Godinho; Teixeira,Anita Fernanda dos Santos; Menezes,Michele Duarte de; Guilherme,Luiz Roberto Guimarães; Moreira,Fatima Maria de Souza; Curi,Nilton. |
| ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Soil analyses; Spatial prediction; Proximal sensor.. |
| Ano: 2017 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1413-70542017000600648 |
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| Souza,Eliana de; Fernandes Filho,Elpídio Inácio; Schaefer,Carlos Ernesto Gonçalves Reynaud; Batjes,Niels H.; Santos,Gerson Rodrigues dos; Pontes,Lucas Machado. |
| ABSTRACT Soil bulk density (ρb) data are needed for a wide range of environmental studies. However, ρb is rarely reported in soil surveys. An alternative to obtain ρb for data-scarce regions, such as the Rio Doce basin in southeastern Brazil, is indirect estimation from less costly covariates using pedotransfer functions (PTF). This study primarily aims to develop region-specific PTFs for ρb using multiple linear regressions (MLR) and random forests (RF). Secondly, it assessed the accuracy of PTFs for data grouped into soil horizons and soil classes. For that purpose, we compared the performance of PTFs compiled from the literature with those developed here. Two groups of data were evaluated as covariates: 1) readily available soil properties and 2) maps... |
| Tipo: Info:eu-repo/semantics/article |
Palavras-chave: Multiple linear regressions; Random forests; Soil predictors; Spatial prediction. |
| Ano: 2016 |
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162016000600525 |
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