Preprocessing procedures and supervised classification applied to a database of systematic soil survey.

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dc.contributorALAN PESSOA VALADARES, IAC; RICARDO MARQUES COELHO, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA.
dc.creatorVALADARES, A. P.
dc.creatorCOELHO, R. M.
dc.creatorOLIVEIRA, S. R. de M.
dc.date2020-01-11T00:41:14Z
dc.date2020-01-11T00:41:14Z
dc.date2020-01-10
dc.date2019
dc.date2020-01-16T11:11:11Z
dc.date.accessioned2026-07-07T04:16:08Z
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.
dc.identifierScientia Agricola, v. 76, n. 5, p. 439-447, Sept./Oct. 2019.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1118563
dc.identifierhttp://dx.doi.org/10.1590/1678-992X-2017-0171
dc.identifier.urihttp://hdl.handle.net/123456789/456012
dc.languageeng
dc.rightsopenAccess
dc.subjectAprendizado de máquina
dc.subjectPré-processamento
dc.subjectClassificação de solos
dc.subjectRandom forest
dc.subjectMachine learning algorithms
dc.subjectTacit soil-landscape relationships
dc.subjectDigital soil mapping
dc.subjectSolo
dc.subjectSoil
dc.subjectSoil classification
dc.titlePreprocessing procedures and supervised classification applied to a database of systematic soil survey.
dc.typeArtigo de periódico

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Embrapa Agricultura Digital (CNPTIA)