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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
27/10/1997 |
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Data da última atualização: |
10/01/2012 |
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Autoria: |
HJELMFELT JÚNIOR, A. T.; WANG, M. |
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Afiliação: |
ALLEN T. HJELMFELT JÚNIOR, USDA Agricultural Research Service; MENGHUA WANG, University of Missouri. |
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Título: |
Runoff hydrograph estimation using artificial neural networks. |
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Ano de publicação: |
1993 |
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Fonte/Imprenta: |
In: HEATWOLE, C. D. (Ed.). Application of advanced information technologies: effective management of natural resources. St. Joseph, MI: ASAE, 1993. |
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Páginas: |
p. 315-320. |
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Idioma: |
Inglês |
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Conteúdo: |
A three-layer, feed-forward artificial neural network was constructed to predict the runoff hydrograph from a given effective rainfall distribution. The network was trained using eight rainfall-runoff events and the trained network tested on sixteen events observed on Goodwater Creek, a 12.2Km² watershed located in north central Missouri. Three networks were tested, all of which contained thirty nodes in the input layer and a single node in the output layer. The effect of varying the number of neurons in the middle layer was investigated by using one, ten and twenty neurons. The network with ten neurons in the middle layer gave the best predictions. |
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Palavras-Chave: |
Artificial neural networks; Chuvas; Hidrogramas; Inteligência artificial; Precipitação; Redes neurais. |
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Thesaurus Nal: |
Hydrology; Rain. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 01398naa a2200241 a 4500
001 1005095
005 2012-01-10
008 1993 bl uuuu u00u1 u #d
100 1 $aHJELMFELT JÚNIOR, A. T.
245 $aRunoff hydrograph estimation using artificial neural networks.
260 $c1993
300 $ap. 315-320.
520 $aA three-layer, feed-forward artificial neural network was constructed to predict the runoff hydrograph from a given effective rainfall distribution. The network was trained using eight rainfall-runoff events and the trained network tested on sixteen events observed on Goodwater Creek, a 12.2Km² watershed located in north central Missouri. Three networks were tested, all of which contained thirty nodes in the input layer and a single node in the output layer. The effect of varying the number of neurons in the middle layer was investigated by using one, ten and twenty neurons. The network with ten neurons in the middle layer gave the best predictions.
650 $aHydrology
650 $aRain
653 $aArtificial neural networks
653 $aChuvas
653 $aHidrogramas
653 $aInteligência artificial
653 $aPrecipitação
653 $aRedes neurais
700 1 $aWANG, M.
773 $tIn: HEATWOLE, C. D. (Ed.). Application of advanced information technologies: effective management of natural resources. St. Joseph, MI: ASAE, 1993.
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Registro original: |
Embrapa Agricultura Digital (CNPTIA) |
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Biblioteca(s): |
Catálogo Coletivo de Periódicos Embrapa; Embrapa Suínos e Aves. |
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Identificador: |
2177 |
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Data corrente: |
09/05/2002 |
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Data da última atualização: |
09/05/2002 |
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Código do título: |
1100090 |
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ISSN: |
0830-9000 |
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Código CCN: |
085439-5 |
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Título e Subtítulo: |
CANADIAN JOURNAL OF VETERINARY RESEARCH |
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Título alternativo: |
REVUE CANADIENNE DE RECHERCHE VETERINAIRE |
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Título anterior: |
CANADIAN JOURNAL OF COMPARATIVE MEDICINE |
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Local de publicação: |
Ottawa-Canada |
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Periodicidade: |
trimestral |
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Bases onde o periódico é indexado: |
INDEX VETERINARIUS; VETERINARY BULLETIN |
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Coleções da unidade: |
Embrapa Suínos e Aves
1986 50(1-4); 1988 52(1,3-4); 1989 53(1-4); 1990 54(1-4, supl); 1991 55(1-4); 1992 56(2-4); 1993 57(1-2); 1995 59(4); 1996 60(2-4); 1997 61(1-4); 1998 62(1-4); 1999 63(1); 2001 65(2-4); 2002 66(1-4); 2003 67(1) |
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