SciELO - Scientific Electronic Library Online

 
vol.44 número12Preliminary DRIS norms for sugarcane developed from a low number of samplesAspectos químicos e estruturais da qualidade fisiológica de sementes de soja índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

Compartilhar


Pesquisa Agropecuária Brasileira

versão impressa ISSN 0100-204X

Resumo

CRIVELENTI, Rafael Castro; COELHO, Ricardo Marques; ADAMI, Samuel Fernando  e  OLIVEIRA, Stanley Robson de Medeiros. Data mining to infer soil-landscape relationships in digital soil mapping. Pesq. agropec. bras. [online]. 2009, vol.44, n.12, pp.1707-1715. ISSN 0100-204X.  http://dx.doi.org/10.1590/S0100-204X2009001200021.

The objective of this work was to develop a methodology for digital soil mapping at a 1:100,000 scale by applying data mining techniques to preexisting relief descriptors and data from pedological and geological maps. A digital database was created from topographic and thematic maps, and allowed the generation of a digital elevation model (DEM) of the Dois Córregos (SP, Brazil) sheet (1:50,000 scale). The slope gradient, slope profile, contour profile, basin contributing area, and diagonal distance to drainage geomorphometric parameters were extracted from the DEM. The matrix which associated this georeferred data was analyzed by means of decision trees within the Weka machine-learning environment, and a model for soil mapping unit prediction was generated. The overall model accuracy increased from 54 to 61% when soil classes with no chances of being predicted were excluded. The association of data mining techniques with geographical information systems produced digital soil maps feasible to be used in studies requiring less detail than those made with the original reference soil maps.

Palavras-chave : decision trees; soil survey; geomorphometric parameters; geographic information system.

        · resumo em Português     · texto em Português     · Português ( pdf )

 

Creative Commons License Todo o conteúdo deste periódico, exceto onde está identificado, está licenciado sob uma Licença Creative Commons