Data/hora: 08/06/2024 08:30:41

Biblioteca(s): | Embrapa Agricultura Digital.
Data corrente: | 27/10/1997
Data da última atualização: | 10/01/2012
Autoria: | HJELMFELT JÚNIOR, A. T.; WANG, M.
Afiliação: | ALLEN T. HJELMFELT JÚNIOR, USDA Agricultural Research Service; MENGHUA WANG, University of Missouri.
Título: | Runoff hydrograph estimation using artificial neural networks.
Ano de publicação: | 1993
Fonte/Imprenta: | In: HEATWOLE, C. D. (Ed.). Application of advanced information technologies: effective management of natural resources. St. Joseph, MI: ASAE, 1993.
Páginas: | p. 315-320.
Idioma: | Inglês
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.
Palavras-Chave: | Artificial neural networks; Chuvas; Hidrogramas; Inteligência artificial; Precipitação; Redes neurais.
Thesaurus Nal: | Hydrology; Rain.
Categoria do assunto: | X Pesquisa, Tecnologia e Engenharia
Registro original: | Embrapa Agricultura Digital (CNPTIA)
Exemplares: | CNPTIA - (5764-1) - ADD - PL - PP - 630.285 - CON - 1997.00026 - DIS