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dc.contributor.authorSILVA, M. A. S. da
dc.contributor.authorMACIEL, R. J. S.
dc.contributor.authorMATOS, L. N.
dc.contributor.authorDOMPIERI, M. H. G.
dc.date.accessioned2019-01-16T23:39:18Z-
dc.date.available2019-01-16T23:39:18Z-
dc.date.created2019-01-14
dc.date.issued2018
dc.identifier.citationModern Environmental Science and Engineering, v. 4, n. 9, p. 872-881, sep. 2018.
dc.identifier.isbn2333-2581
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1103941-
dc.descriptionThis article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic environmental zoning by combining the handle of categorical data and the other for automatic clustering. The SOM online learning algorithm had been chosen to treat categorical data by using the dot product method and the Sorense-Dice binary similarity coefficient. To automatically perform a spatial clustering, an adaptation of the automatic clustering Costa-Netto algorithm had been also proposed. The correspondence analysis had been used to examine the profiles of each homogeneous zones. To explore the approach it has been performed the environmental zoning of the Alto Taquari River Basin, Brazil, using as input data a set of thematic maps. The results indicate the applicability of the approach to perform the exploratory environmental zoning.
dc.language.isoporpt_BR
dc.rightsopenAccesspt_BR
dc.subjectArtificial neural network
dc.subjectExploratory spatial analysis
dc.subjectSimilarity coefficients
dc.subjectAlto Taquari river
dc.titleAutomatic environmental zoning with self-organizing maps.
dc.typeArtigo de periódico
dc.date.updated2019-04-26T11:11:11Zpt_BR
dc.subject.nalthesaurusCorrespondence analysis
riaa.ainfo.id1103941
riaa.ainfo.lastupdate2019-04-26 -03:00:00
dc.identifier.doi10.15341/mese(2333-2581)/09.04.2018/011
dc.contributor.institutionMARCOS AURELIO SANTOS DA SILVA, CPATC; RENATO JOSE SANTOS MACIEL, CNPTIA; LEONARDO N. MATOS, UNIVERSIDADE FEDERAL DO SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM.
Aparece en las colecciones:Artigo em periódico indexado (CNPM)

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