Artificial neural networks for streamflow prediction

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dc.contributor.authorDolling, OR
dc.contributor.authorVaras, EA
dc.date.accessioned2024-01-10T12:04:19Z
dc.date.available2024-01-10T12:04:19Z
dc.date.issued2002
dc.description.abstractThis paper presents monthly streamflow prediction using artificial neural networks (ANN) on mountain watersheds. The procedure addresses the selection of input variables, the definition of model architecture and the strategy of the learning process. Results show that spring and summer monthly streamflows can be adequately represented, improving the results of calculations obtained using other methods. Better streamflow prediction methods should have significant benefits for the optimal use of water resources for irrigation and hydroelectric energy generation.
dc.fechaingreso.objetodigital2024-05-06
dc.format.extent8 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1080/00221680209499899
dc.identifier.eissn1814-2079
dc.identifier.issn0022-1686
dc.identifier.urihttps://doi.org/10.1080/00221680209499899
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/75760
dc.identifier.wosidWOS:000179153100001
dc.information.autorucIngeniería;Varas E;S/I;98431
dc.issue.numero5
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final554
dc.pagina.inicio547
dc.publisherTAYLOR & FRANCIS LTD
dc.revistaJOURNAL OF HYDRAULIC RESEARCH
dc.rightsacceso restringido
dc.subject.ods13 Climate Action
dc.subject.ods06 Clean Water and Sanitation
dc.subject.odspa13 Acción por el clima
dc.subject.odspa06 Agua limpia y saneamiento
dc.titleArtificial neural networks for streamflow prediction
dc.typeartículo
dc.volumen40
sipa.codpersvinculados98431
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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