Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Territorial. |
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Data corrente: |
14/01/2019 |
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Data da última atualização: |
10/12/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
B - 4 |
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Autoria: |
SILVA, M. A. S. da; MACIEL, R. J. S.; MATOS, L. N.; DOMPIERI, M. H. G. |
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Afiliação: |
MARCOS AURELIO SANTOS DA SILVA, CPATC; RENATO JOSE SANTOS MACIEL, CNPTIA; LEONARDO N. MATOS, UNIVERSIDADE FEDERAL DO SERGIPE; MARCIA HELENA GALINA DOMPIERI, CNPM. |
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Título: |
Automatic environmental zoning with self-organizing maps. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Modern Environmental Science and Engineering, v. 4, n. 9, p. 872-881, Sept. 2018. |
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ISBN: |
2333-2581 |
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DOI: |
10.15341/mese(2333-2581)/09.04.2018/011 |
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Idioma: |
Inglês
Português |
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Conteúdo: |
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. |
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Palavras-Chave: |
Alto Taquari river; Alto Taquari river basin; Análise de correspondência; Artificial neural network; Exploratory spatial analysis; Redes neurais artificiais; Similarity coefficients. |
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Thesaurus NAL: |
Correspondence analysis; Neural networks. |
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Categoria do assunto: |
P Recursos Naturais, Ciências Ambientais e da Terra |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/190432/1/5030.pdf
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Marc: |
LEADER 01797naa a2200289 a 4500
001 2127961
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008 2018 bl uuuu u00u1 u #d
022 $a2333-2581
024 7 $a10.15341/mese(2333-2581)/09.04.2018/011$2DOI
100 1 $aSILVA, M. A. S. da
245 $aAutomatic environmental zoning with self-organizing maps.$h[electronic resource]
260 $c2018
520 $aThis 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.
650 $aCorrespondence analysis
650 $aNeural networks
653 $aAlto Taquari river
653 $aAlto Taquari river basin
653 $aAnálise de correspondência
653 $aArtificial neural network
653 $aExploratory spatial analysis
653 $aRedes neurais artificiais
653 $aSimilarity coefficients
700 1 $aMACIEL, R. J. S.
700 1 $aMATOS, L. N.
700 1 $aDOMPIERI, M. H. G.
773 $tModern Environmental Science and Engineering$gv. 4, n. 9, p. 872-881, Sept. 2018.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
