Monthly river flow forecasting by an adaptive neuro-fuzzy inference system
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2010
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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.
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Goksu River , Turkey , Catchments , Forecasting , Fuzzy inference , Fuzzy systems , Neural networks , Stream flow , river water , Adaptive neuro-fuzzy inference system , ANFIS , ANFIS method , ANFIS model , Autoregressive methods , Best-fit models , Forecasting models , Monthly river flow , River flow , River flow forecasting , Time periods , Training and testing , Validation data , accuracy assessment , artificial neural network , forecasting method , fuzzy mathematics , hydrological modeling , river flow , article , artificial neural network , catchment , environmental monitoring , forecasting , fuzzy logic , fuzzy system , hydropower , irrigation (agriculture) , model , positive feedback , priority journal , river ecosystem , Turkey (republic) , Rivers