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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
13/12/2011 |
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Data da última atualização: |
24/01/2020 |
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Tipo da produção científica: |
Artigo em Anais de Congresso |
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Autoria: |
SILVA, C. C. da; OLIVEIRA, S. R. de M.; ADAMI, S. F.; CRIVELENTI, R. C.; COELHO, R. M. |
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Afiliação: |
CRISTIANO CASSIANO DA SILVA, IAC; STANLEY ROBSON DE MEDEIROS OLIVEIRA, CNPTIA; SAMUEL FERNANDO ADAMI, IAC; RAFAEL CASTRO CRIVELENTI, SMA-SP; RICARDO MARQUES COELHO, IAC. |
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Título: |
Diferentes classificadores na predição de classes de solos em mapeamento digital. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
In: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: Inpe, 2011. |
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Páginas: |
p. 9088-9095. |
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Idioma: |
Inglês |
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Notas: |
SBSR 2011. |
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Conteúdo: |
ABSTRACT: The study had as objective to develop techniques for digital soil mapping with support of main parameters of relief descriptors, of geologic map and pedological map pre-existing of Dois Córregos (SP, BRAZIL) sheet (1:50,000 scale), using data mining techniques. It was built a database from digital topographic and thematic, and data from soils and geology. Were calculate geomorphometric slope, curvature in plan and in profile and diagonal distance of the drainage area of study. These parameters and the geological map units were crossed through georeferencing the pedological map, enabling the construction of a matrix relating soil mapping units with original caption and simplified legend to the topography and geology parameters of reference areas.. This matrix was analyzed by three different techniques of machine learning, decision trees, k-NN and Naive Bayes, who predicted the soil mapping units. We evaluated soil mapping units accuracy individually and overall maps accuracy. Our results demonstrate that increasing number of records for training the algorithm increased the individual mapping units accuracy and maps. The decision tree algorithms and k-NN had the highest accuracy in both types of legend, but low in relation to training maps. |
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Palavras-Chave: |
Data mining; Mineração de dados; Sistemas de informação geográfica; Solos. |
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Thesagro: |
Pedologia. |
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Thesaurus Nal: |
Geographic information systems; Soil. |
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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/50076/1/diferentes.pdf
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Marc: |
LEADER 02115nam a2200265 a 4500
001 1909476
005 2020-01-24
008 2011 bl uuuu u00u1 u #d
100 1 $aSILVA, C. C. da
245 $aDiferentes classificadores na predição de classes de solos em mapeamento digital.$h[electronic resource]
260 $aIn: SIMPÓSIO BRASILEIRO DE SENSORIAMENTO REMOTO, 15., 2011, Curitiba. Anais... São José dos Campos: Inpe$c2011
300 $ap. 9088-9095.
500 $aSBSR 2011.
520 $aABSTRACT: The study had as objective to develop techniques for digital soil mapping with support of main parameters of relief descriptors, of geologic map and pedological map pre-existing of Dois Córregos (SP, BRAZIL) sheet (1:50,000 scale), using data mining techniques. It was built a database from digital topographic and thematic, and data from soils and geology. Were calculate geomorphometric slope, curvature in plan and in profile and diagonal distance of the drainage area of study. These parameters and the geological map units were crossed through georeferencing the pedological map, enabling the construction of a matrix relating soil mapping units with original caption and simplified legend to the topography and geology parameters of reference areas.. This matrix was analyzed by three different techniques of machine learning, decision trees, k-NN and Naive Bayes, who predicted the soil mapping units. We evaluated soil mapping units accuracy individually and overall maps accuracy. Our results demonstrate that increasing number of records for training the algorithm increased the individual mapping units accuracy and maps. The decision tree algorithms and k-NN had the highest accuracy in both types of legend, but low in relation to training maps.
650 $aGeographic information systems
650 $aSoil
650 $aPedologia
653 $aData mining
653 $aMineração de dados
653 $aSistemas de informação geográfica
653 $aSolos
700 1 $aOLIVEIRA, S. R. de M.
700 1 $aADAMI, S. F.
700 1 $aCRIVELENTI, R. C.
700 1 $aCOELHO, R. M.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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| Registros recuperados : 3 | |
| 2. |  | CRIVELENTI, R. C.; COELHO, R. M.; ADAMI, S. F.; OLIVEIRA, S. R. de M. Mineração de dados para inferência de relações solo-paisagem em mapeamentos digitais de solo. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 44, n. 12, p.1707-1715, dez. 2009| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Agricultura Digital; Embrapa Unidades Centrais. |
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| Registros recuperados : 3 | |
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