Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1118563
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dc.contributor.authorVALADARES, A. P.eng
dc.contributor.authorCOELHO, R. M.eng
dc.contributor.authorOLIVEIRA, S. R. de M.eng
dc.date.accessioned2020-01-11T00:41:14Z-
dc.date.available2020-01-11T00:41:14Z-
dc.date.created2020-01-10
dc.date.issued2019
dc.identifier.citationScientia Agricola, v. 76, n. 5, p. 439-447, Sept./Oct. 2019.eng
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1118563-
dc.descriptionABSTRACT:Data Mining techniques play an important role in the prediction of soil spatial distribution in systematic soil surveying, though existing methodologies still lack standardization and a full understanding of their capabilities. The aim of this work was to evaluate the performance of preprocessing procedures and supervised classification approaches for predicting map units from 1:100,000-scale conventional semi-detailed soil surveys. Sheets of the Brazilian National Cartographic System on the 1:50,000 scale, ?Dois Córregos? (?Brotas? 1:100,000-scale sheet), ?São Pedro? and ?Laras? (?Piracicaba? 1:100,000-scale sheet) were used for developing models. Soil map information and predictive environmental covariates for the dataset were obtained from the semi-detailed soil survey of the state of São Paulo, from the Brazilian Institute of Geography and Statistics (IBGE) 1:50,000-scale topographic sheets and from the 1:750,000-scale geological map of the state of São Paulo. The target variable was a soil map unit of four types: local ?soil unit? name and soil class at three hierarchical levels of the Brazilian System of Soil Classification (SiBCS). Different data preprocessing treatments and four algorithms all having different approaches were also tested. Results showed that composite soil map units were not adequate for the machine learning process. Class balance did not contribute to improving the performance of classifiers. Accuracy values of 78 % and a Kappa index of 0.67 were obtained after preprocessing procedures with Random Forest, the algorithm that performed best. Information from conventional map units of semi-detailed (4th order) 1:100,000 soil survey generated models with values for accuracy, precision, sensitivity, specificity and Kappa indexes that support their use in programs for systematic soil surveying.eng
dc.language.isoengeng
dc.rightsopenAccesseng
dc.subjectAprendizado de máquinaeng
dc.subjectPré-processamentoeng
dc.subjectClassificação de soloseng
dc.subjectRandom foresteng
dc.subjectMachine learning algorithmseng
dc.subjectTacit soil-landscape relationshipseng
dc.subjectDigital soil mappingeng
dc.titlePreprocessing procedures and supervised classification applied to a database of systematic soil survey.eng
dc.typeArtigo de periódicoeng
dc.date.updated2020-01-16T11:11:11Z
dc.subject.thesagroSoloeng
dc.subject.nalthesaurusSoileng
dc.subject.nalthesaurusSoil classificationeng
riaa.ainfo.id1118563eng
riaa.ainfo.lastupdate2020-01-16 -02:00:00
dc.identifier.doihttp://dx.doi.org/10.1590/1678-992X-2017-0171eng
dc.contributor.institutionALAN PESSOA VALADARES, IAC; RICARDO MARQUES COELHO, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA.eng
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