Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/909476
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dc.contributor.authorSILVA, C. C. dapt_BR
dc.contributor.authorOLIVEIRA, S. R. de M.pt_BR
dc.contributor.authorADAMI, S. F.pt_BR
dc.contributor.authorCRIVELENTI, R. C.pt_BR
dc.contributor.authorCOELHO, R. M.pt_BR
dc.date.accessioned2011-12-13T11:11:11Zpt_BR
dc.date.accessioned2011-12-13T11:11:11Zpt_BR
dc.date.available2011-12-13T11:11:11Zpt_BR
dc.date.available2011-12-13T11:11:11Zpt_BR
dc.date.created2011-12-13pt_BR
dc.date.issued2011pt_BR
dc.identifier.citationIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: Inpe, 2011.pt_BR
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/909476pt_BR
dc.descriptionABSTRACT: The study had as objective to develop techniques for digital soil mapping with support of main parameters of relief descriptors, of geologic map and pedological map pre-existing of Dois Córregos (SP, BRAZIL) sheet (1:50,000 scale), using data mining techniques. It was built a database from digital topographic and thematic, and data from soils and geology. Were calculate geomorphometric slope, curvature in plan and in profile and diagonal distance of the drainage area of study. These parameters and the geological map units were crossed through georeferencing the pedological map, enabling the construction of a matrix relating soil mapping units with original caption and simplified legend to the topography and geology parameters of reference areas.. This matrix was analyzed by three different techniques of machine learning, decision trees, k-NN and Naive Bayes, who predicted the soil mapping units. We evaluated soil mapping units accuracy individually and overall maps accuracy. Our results demonstrate that increasing number of records for training the algorithm increased the individual mapping units accuracy and maps. The decision tree algorithms and k-NN had the highest accuracy in both types of legend, but low in relation to training maps.pt_BR
dc.language.isoengpt_BR
dc.rightsopenAccesseng
dc.subjectSolospt_BR
dc.subjectMineração de dadospt_BR
dc.subjectSistemas de informação geográficapt_BR
dc.subjectData miningeng
dc.titleDiferentes classificadores na predição de classes de solos em mapeamento digital.pt_BR
dc.typeArtigo em anais e proceedingspt_BR
dc.date.updated2020-01-24T11:11:11Zpt_BR
dc.subject.thesagroPedologiapt_BR
dc.subject.nalthesaurusSoileng
dc.subject.nalthesaurusGeographic information systemseng
dc.description.notesSBSR 2011.pt_BR
dc.format.extent2p. 9088-9095.pt_BR
riaa.ainfo.id909476pt_BR
riaa.ainfo.lastupdate2020-01-24 -02:00:00pt_BR
dc.contributor.institutionCRISTIANO CASSIANO DA SILVA, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; SAMUEL FERNANDO ADAMI, IAC; RAFAEL CASTRO CRIVELENTI, SMA-SP; RICARDO MARQUES COELHO, IAC.pt_BR
Aparece nas coleções:Artigo em anais de congresso (CNPTIA)

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