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Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

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dc.contributor.author Firat, M.
dc.contributor.author Turan, M.E.
dc.date.accessioned 2019-08-16T12:06:50Z
dc.date.available 2019-08-16T12:06:50Z
dc.date.issued 2010
dc.identifier.issn 17476585 (ISSN)
dc.identifier.uri http://acikerisim.pau.edu.tr:8080/xmlui/handle/11499/6391
dc.description.abstract In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997-2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting. © 2009 The Authors. Journal compilation.
dc.language.iso English
dc.relation.isversionof 10.1111/j.1747-6593.2008.00162.x
dc.subject ANFIS
dc.subject Fuzzy logic
dc.subject Göksu River
dc.subject Monthly river flow
dc.subject River flow forecasting.
dc.subject Adaptive neuro-fuzzy inference system
dc.subject ANFIS method
dc.subject ANFIS model
dc.subject Autoregressive methods
dc.subject Best-fit models
dc.subject Forecasting models
dc.subject River flow
dc.subject River flow forecasting
dc.subject Time periods
dc.subject Training and testing
dc.subject Validation data
dc.subject Catchments
dc.subject Forecasting
dc.subject Fuzzy inference
dc.subject Fuzzy systems
dc.subject Neural networks
dc.subject Stream flow
dc.subject Rivers
dc.subject river water
dc.subject accuracy assessment
dc.subject artificial neural network
dc.subject forecasting method
dc.subject fuzzy mathematics
dc.subject hydrological modeling
dc.subject river flow
dc.subject article
dc.subject catchment
dc.subject environmental monitoring
dc.subject forecasting
dc.subject fuzzy logic
dc.subject fuzzy system
dc.subject hydropower
dc.subject irrigation (agriculture)
dc.subject model
dc.subject positive feedback
dc.subject priority journal
dc.subject river ecosystem
dc.subject Turkey (republic)
dc.subject Goksu River
dc.subject Turkey
dc.title Monthly river flow forecasting by an adaptive neuro-fuzzy inference system
dc.type Article
dc.relation.journal Water and Environment Journal
dc.identifier.volume 24
dc.identifier.issue 2
dc.identifier.startpage 116
dc.identifier.endpage 125
dc.identifier.index Scopus


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