Performance enhancement of a conceptual hydrological model by integrating artificial intelligence

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dc.contributor.authorKumanlioglu A.A.
dc.contributor.authorFistikoglu O.
dc.date.accessioned2024-07-22T08:08:17Z
dc.date.available2024-07-22T08:08:17Z
dc.date.issued2019
dc.description.abstractA daily rainfall-runoff model has been improved by the integration of artificial neural network (ANN) and genetic algorithm (GA). The integrations are carried out on the daily rainfall-runoff model Génie rural à 4 paramètres journalier (GR4J). GR4J consists of production and routing storages. The production storage has only one process parameter and the routing storage has three. The ANN integration eliminates the three routing parameters. Automatic calibration capability has been added to the new hybrid model by integrating GA. The new hybrid model, which uses antecedent rainfall and temperature series, is applied to the Gediz River Basin in western Turkey. The results reveal that the hybrid model has better prediction performance than the original GR4J as well as the single ANN-based runoff prediction model. © 2019 American Society of Civil Engineers.
dc.identifier.DOI-ID10.1061/(ASCE)HE.1943-5584.0001850
dc.identifier.issn10840699
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/14329
dc.language.isoEnglish
dc.publisherAmerican Society of Civil Engineers (ASCE)
dc.subjectGediz Basin
dc.subjectTurkey
dc.subjectClimate models
dc.subjectNeural networks
dc.subjectRain
dc.subjectRunoff
dc.subjectWatersheds
dc.subjectAntecedent rainfall
dc.subjectAutomatic calibration
dc.subjectGediz River
dc.subjectHydrological modeling
dc.subjectPerformance enhancements
dc.subjectPrediction performance
dc.subjectProcess parameters
dc.subjectRunoff prediction model
dc.subjectartificial intelligence
dc.subjectartificial neural network
dc.subjectcomputer simulation
dc.subjectconceptual framework
dc.subjecthydrological modeling
dc.subjectnumerical model
dc.subjectrainfall-runoff modeling
dc.subjectGenetic algorithms
dc.titlePerformance enhancement of a conceptual hydrological model by integrating artificial intelligence
dc.typeArticle

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