Artificial Neural Network Approaches For Inflow Estimation At Adiguzel Dam, Turkey

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dc.authoridTR36429en_US
dc.contributor.authorFirat, Mahmut
dc.date.accessioned2015-04-07T08:13:17Z
dc.date.available2015-04-07T08:13:17Z
dc.date.issued2008
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractAccurate modeling and forecasting of hydrological processes such as rainfall, rainfall run off relationship, runoff, is important for management and planning of water resources. In this study, applicability of Artificial Neural Network (ANN) methods for inflow forecasting at Adıgüzel dam on the Büyük Menderes River catchment was investigated. For this purpose, daily river flow data points were obtained from two river flow gauge stations on the Büyük Menderes river, Çıtak Köprü (713), Dört Değirmen (735), over 12 years, 1988 - 2000, and models having various input structures were constructed. The two different types of ANN, Feed Forward Neural Networks and Radial Basis Neural Networks, have been used to forecast daily river flow. The models were trained and tested by FFNN and RBNN and the results of models were compared with field observation data. Criteria for performance evaluation were identified in order to evaluate and compare the performances of FFNN and RBNN models. Then the best fit model and network structure were determined according to these criteria.en_US
dc.identifier.citationJournal of Science and Technology 2 (2), 2008, 278-292tr_TR
dc.identifier.issn1307-3818
dc.language.isoenen_US
dc.publisherBeykent Üniversitesitr_TR
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectInflowtr_TR
dc.subjectANNtr_TR
dc.subjectFeed Forward Neural Networkstr_TR
dc.subjectRadial Basis Neural Networkstr_TR
dc.subjectAdıgüzel Damtr_TR
dc.subjectBüyük Menderes Catchmenttr_TR
dc.titleArtificial Neural Network Approaches For Inflow Estimation At Adiguzel Dam, Turkeyen_US
dc.typeArticleen_US

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