Uncertainty Evaluation of ANN and ANFIS Models in Inflow Forecasting into the Raees-Ali Delvari Dam

Subject Areas : Water Resource Management

Ali Eskandari ¹ , Roohollah Noori ² , Mohammad Reza Vesali Naseh ³ , Farimah Saeedi ⁴

1 - Lecturer in Civil and Environmental Engineering, Department of Civil Engineering, Boushehr Branch, Islamic Azad University, Boushehr, Iran (Corresponding Author)
2 - Assistant Professor, Environmental Engineering, School of Environment, College of Engineering, University of Tehran, Tehran, Iran
3 - Assistant Professor , Environmental Engineering, Department of Civil Engineering, Arak University, Arak, Iran
4 - - Staff, Environmental Engineering, School of Environment, College of Engineering, University of Tehran, Tehran, Iran

Received: 2016-04-08 Accepted : 2016-10-31 Published : 2019-09-23

Keywords: Shahpour River, Raees-Ali Delvari Dam, Inflow Forecasting, Uncertainty Analysis,

Abstract :

Background and Objective: Accurate information about the river flow significantly influences the water resources management for the communities that use the water. In this regard, this study aims to present a reliable prediction of the monthly discharge of Shahpour River, inflow to Raees-Ali Delvari Dam, located in the Boushehr Province, Iran. Methods: To forecast the monthly inflow to Raees-Ali Delvari Dam, the artificial intelligence models, i.e. artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS), were applied. Also, uncertainty determination of the both models was carried out in order to improve the application of their results in the management decisions in the water sector. In this regard, the simulated results of the models, tuned with the different pattern of calibration data, were used. Two indices, i.e. the width of confidence band (d-factor) and the values bracketed by 95 percent prediction uncertainties (95PPU) were applied in order to evaluate the models’ uncertainty.  Findings: Results of tuned ANN and ANFIS models indicated that although the both models had the appropriate values of determination coefficient (R2) and mean absolute error (MAE), their performance was along with considerable errors in the high extreme values. Besides, a look at through the uncertainty results of the models indicated the ANFIS model, that included the less d-factor and higher 95PPU values, had less uncertainty than the ANN. Discussion and Conclusion: Considering the same performance of the both ANN and ANFIS models in the calibration and test steps, it can be concluded that the ANFIS model was the best selection for monthly inflow prediction into Raees-Ali Delvari Dam due to its less uncertainty that ANN model.

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