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dc.contributor.authorTIKHAMARINE, Yazid-
dc.contributor.authorSOUAG-GAMANE, Doudja-
dc.contributor.authorMELLAK, Soumia-
dc.contributor.authorCHERGUI, Sakina-
dc.date.accessioned2021-04-05T00:58:16Z-
dc.date.available2021-04-05T00:58:16Z-
dc.date.issued2019-10-16-
dc.identifier.urihttp://dspace.univ-ouargla.dz/jspui/handle/123456789/25334-
dc.descriptionSéminaire International sur l′Hydrogéologie et l′Environnement SIHE 2019 Ouarglaen_US
dc.description.abstractpredicting streamflow values accurately is vitally important for hydrology and hydrogeology in water resources management system. Daily and monthly streamflow prediction can help in water management domain, regulation distribution of dams and estimation of groundwater level, especially in drought and flood issues. Streamflow forecast contributes to improve long and short-term time series by using previous information, therefore a power performance model should be used to processen_US
dc.publisherUniversité Kasdi Merbah Ouarglaen_US
dc.subjectdiscrete wavelet transformen_US
dc.subjectStreamflow predictionen_US
dc.subjectartificial neural networken_US
dc.subjectSeybouse watersheden_US
dc.titleStreamflow prediction using a new approach of hybrid artificial neural network with discrete wavelet transform. A case study: the catchment of Seybouse in northeastern Algeriaen_US
dc.typeArticleen_US
Appears in Collections:4. Faculté des Hydrocarbures, des Energies Renouvelables, des Sciences de la Terre et de l’Univers

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