Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1103941
Research center of Embrapa/Collection: Embrapa Territorial - Artigo em periódico indexado (ALICE)
Date Issued: 2018
Type of Material: Artigo em periódico indexado (ALICE)
Authors: SILVA, M. A. S. da
MACIEL, R. J. S.
MATOS, L. N.
DOMPIERI, M. H. G.
Additional Information: MARCOS AURELIO SANTOS DA SILVA, CPATC; RENATO JOSE SANTOS MACIEL, CNPTIA; LEONARDO N. MATOS, UNIVERSIDADE FEDERAL DO SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM.
Title: Automatic environmental zoning with self-organizing maps.
Publisher: Modern Environmental Science and Engineering, v. 4, n. 9, p. 872-881, sep. 2018.
Language: pt_BR
Keywords: Artificial neural network
Exploratory spatial analysis
Similarity coefficients
Alto Taquari river
Description: This 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.
NAL Thesaurus: Correspondence analysis
Data Created: 2019-01-14
ISBN: 2333-2581
Appears in Collections:Artigo em periódico indexado (CNPM)

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