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Prediction of Darbandikhan Reservoir Inflow Using ANFIS Models.

Hekmat M. Ibrahim

Faculty of Engineering-Sulaimani University, Bakrajo Street, Sulaimaniyah-lraq


Original: 1 October 2016, Revised: 23 December 2016, Accepted: 9 January 2017, Published online: 20 June 2017


Predicting reservoir inflow is important to reservoir management and scheduling; therefore, accurate prediction of reservoir inflow has been a vital task for researchers and water resources managers during last decades. Recently, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been extensively used to find the relationship between inputs and outputs without considering the physical process of the phenomena. Therefore, the current study investigates and evaluates the capability and applicability of the ANFIS technique in modeling the reservoir inflow for predicting future values of monthly inflow to Darbandikhan reservoir in Kurdistan Region, Iraq. Five rain gage stations data from 1992-2009 were used to develop ANFIS models to predict monthly reservoir inflow from various combinations of antecedent values of inflow and rainfall within the basin in Iraq and Iran which are considered as input variables. Two different membership functions (MFs), triangular and generalized bell, with different numbers (2-5) for each input variable were investigated in the ANFIS models. The best fit models were selected using three performance evaluation criteria, namely; coefficient of determination ( R 2
), coefficient of efficiency ( CE ) and normalized root mean squared error (  NRMSE  ). In comparison of ANFIS model uses the triangular MF with that model uses the generalized bell MF, approximately, similar predict accuracies were obtained for both MFs except the latter needs fewer numbers of MFs. The results indicated that using a combination of inflow and rainfall as input variables is effective in improving predict accuracy and developing parsimonious models with fewer and readily available input variables. Moreover, among different architectures of ANFIS, structures, including three input variables, 1 and 12 time lags of inflow ( It-1 , It-12  ) and rainfall of Marivan station ( ) showed better performance for this application. For best models, the performance evaluation criteria, , and , for checking data set were obtained as 0.96, 0.95 and 0.2 respectively for model with four triangular MFs; and as 0.96, 0.96 and 0.2 respectively for model with three generalized bell MFs. Reservoir inflow modeling in this way will be more reliable than doing it using a time series model as a more effective parameter could be incorporated. By considering the results, ANFIS method is an effective tool that can be successfully applied for reservoir inflow modeling and have satisfactory performances in Darbandikhan reservoir monthly inflow prediction.

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