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| Registros recuperados : 959 | |
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Registro Completo
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Biblioteca(s): |
Embrapa Agricultura Digital; Embrapa Tabuleiros Costeiros. |
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Data corrente: |
06/09/2017 |
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Data da última atualização: |
21/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
MACIEL, R. J. S.; SILVA, M. A. S. da; MATOS, L. N.; DOMPIERI, M. H. G. |
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Afiliação: |
RENATO JOSE SANTOS MACIEL, CNPTIA; MARCOS AURELIO SANTOS DA SILVA, CPATC; UFS; MARCIA HELENA GALINA DOMPIERI, CPATC. |
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Título: |
A neural qualitative approach for automatic territorial zoning. |
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Ano de publicação: |
2017 |
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Fonte/Imprenta: |
A neural qualitative approach for automatic territorial zoning. In: INTERNATIONAL CONFERENCE ON GEOCOMPUTATION, 21., 2017, Leeds. Celebrating 21 years of GeoComputation: extended abstracts. Leeds: University of Leeds, 2017. |
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Páginas: |
p. 1-7. |
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Idioma: |
Inglês
Português |
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Notas: |
GeoComputation 2017. |
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Conteúdo: |
This article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic territorial 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 territorial 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 territorial zoning. |
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Palavras-Chave: |
Alto Taquari River Basin; Análise espacial; Bacia do Alto Taquari; Exploratory spatial analysis; Maps; Self-organizing maps; Similarity coefficients; Zoneamento. |
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Thesagro: |
Mapa; Recurso hídrico; Rio. |
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Thesaurus NAL: |
Correspondence analysis; Thematic maps; Zoning. |
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Categoria do assunto: |
--
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/171403/1/Neural-quantitative-Maciel-Geocomputing.pdf
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Marc: |
LEADER 01979nam a2200337 a 4500
001 2085890
005 2020-01-21
008 2017 bl uuuu u00u1 u #d
100 1 $aMACIEL, R. J. S.
245 $aA neural qualitative approach for automatic territorial zoning.$h[electronic resource]
260 $aA neural qualitative approach for automatic territorial zoning. In: INTERNATIONAL CONFERENCE ON GEOCOMPUTATION, 21., 2017, Leeds. Celebrating 21 years of GeoComputation: extended abstracts. Leeds: University of Leeds$c2017
300 $ap. 1-7.
500 $aGeoComputation 2017.
520 $aThis article presents the application of the Self-Organizing Maps (SOM) as an exploratory tool for automatic territorial 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 territorial 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 territorial zoning.
650 $aCorrespondence analysis
650 $aThematic maps
650 $aZoning
650 $aMapa
650 $aRecurso hídrico
650 $aRio
653 $aAlto Taquari River Basin
653 $aAnálise espacial
653 $aBacia do Alto Taquari
653 $aExploratory spatial analysis
653 $aMaps
653 $aSelf-organizing maps
653 $aSimilarity coefficients
653 $aZoneamento
700 1 $aSILVA, M. A. S. da
700 1 $aMATOS, L. N.
700 1 $aDOMPIERI, M. H. G.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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