Consulta: creatorFacets:"Campos,Alcinei Ribeiro"
Registros recuperados: 5
Data/hora: 02/06/2024 16:48:20

Individualization of soil classes by disaggregation of physiographic map polygons
Provedor de dados: 86
Autores: 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

Spatial Disaggregation of Multi-Component Soil Map Units Using Legacy Data and a Tree-Based Algorithm in Southern Brazil
Provedor de dados: 90
Autores: Machado,Israel Rosa; Giasson,Elvio; Campos,Alcinei Ribeiro; Costa,José Janderson Ferreira; Silva,Elisângela Benedet da; Bonfatti,Benito Roberto.
ABSTRACT Soil surveys often contain multi-component map units comprising two or more soil classes, whose spatial distribution within the map unit is not represented. Digital Soil Mapping tools supported by information from soil surveys make it possible to predict where these classes are located. The aim of this study was to develop a methodology to increase the detail of conventional soil maps by means of spatial disaggregation of multi-component map units and to predict the spatial location of the derived soil classes. Three digital maps of terrain variables - slope, landforms, and topographic wetness index - were correlated with the soil map and 72 georeferenced profiles from the Porto Alegre soil survey. Explicit rules that expressed regional...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Digital soil mapping; Soil-landscape relationships; Decision trees.
Ano: 2018
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-06832018000100303

Selection of Environmental Covariates for Classifier Training Applied in Digital Soil Mapping
Provedor de dados: 90
Autores: 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

Equações de intensidade-duração-frequência de chuvas para o estado do Piauí
Provedor de dados: 124
Autores: Campos,Alcinei Ribeiro; Santos,Glenio Guimarães; Silva,João Batista Lopes; Irene Filho,João; Loura,Domingos de Sousa.
Neste trabalho objetivou-se ajustar e comparar os parâmetros (K, a, b e c) da equação de intensidade-duração-frequência para precipitações pluviais máximas de 133 estações pluviométricas localizadas no estado do Piauí, disponíveis no banco de dados da Agência Nacional de Águas - ANA. Inicialmente, foi realizada a seleção das estações através de analise de consistência das séries de dados, a partir do qual foram selecionadas 105 estações pluviométricas. As chuvas máximas de um dia foram desagregadas em duração de 5; 10; 15; 20; 25; 30; 60; 360; 480; 600; 720 e 1.440 minutos e estimadas as chuvas máximas através de modelos probabilísticos para cada duração e período de retorno de 5; 10; 25; 50 e 100 anos. Em seguida, fez-se o ajuste dos parâmetros (K, a, b e...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Hidrologia; Precipitação; Chuvas-Frequência da intensidade.
Ano: 2014
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902014000300008

Digital soil class mapping in Brazil: a systematic review
Provedor de dados: 63
Autores: Coelho,Fabrício Fernandes; Giasson,Elvio; Campos,Alcinei Ribeiro; Tiecher,Tales; Costa,José Janderson Ferreira; Coblinski,João Augusto.
ABSTRACT: In Brazil several digital soil class mapping studies were carried out from 2006 onwards to maximize the use of existing maps and information and to provide estimates for wider areas. However, there is no consensus on which methods have produced superior results in the predictive value of soil maps. This study conducts a systematic review of digital soil class mapping in Brazil and aims to analyze the factors which can improve the accuracy of digital soil class maps. Data from 334 digital soil class mapping studies were grouped and analyzed by Student's t-test, Wilcoxon-Mann-Whitney test and Kruskal-Wallis test. When conventional maps were used for validation, the studies showed average values of 63 % and when field samples were used, 56 % for...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: Pedology; Mapping unit density; Artificial neural networks; Soil-forming factors; Overall accuracy.
Ano: 2021
URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162021000501401