Evaluation of the Application of Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference Systems for Rainfall-Runoff Modelling in Zayandeh_rood Dam Basin

Document Type : Research Paper


1 Assoc. Prof., Dept. of Natural Resources and Desert Studies, Yazd University, Yazd

2 Former Grad. Student of Watershed Management, Dept. of Natural Resources and Desert Studies, Yazd University, Yazd

3 Assist. Prof. of Watershed Management, Dept. of Natural Resources and Desert Studies, Yazd University, Yazd

4 Assist. Prof., Dept. of Natural Resources, Zabol University, Zabol


During recent few decades, due to the importance of the availability of water, and therefore the necesity of predicting run off resulted from rain fall there has been an increase in developing and implementation of new suitable method for prediction of run off using precipitation data. One of these approaches that have been developed in several areas of sciences including water related fields, is soft computing techniques such as artificial neural networks and fuzzy logic systems. This research was designed to evaluate the applicability of artificial neural network and adaptive neuro –fuzzy inference system to model rainfall-runoff process in Zayandeh_rood dam basin. It must be mentioned that, data have been analysed using Wingamma software, to select appropriate type and number of training input data before they can be used in the models. Then, it has been tried to evaluated applicability of artificial neural networks and neuro-fuzzy techniques to predict runoff generated from daily rainfall. Finally, the accuracy of the results produced by these methods has been compared using statistical criterion. Results taken from this research show that artificial neural networks and neuro-fuzzy technique presented different outputs in different conditions in terms of type and number of inputs variables, but both method have been able to produce acceptable results when suitable input variables and network structures are used.


Akbarpour, M., Rahnama, M. B., and Barani, Gh. A. (2003). Comparison of artifical neural network and HEC-HMS models on rainfall-runoff process. 4th Iranian Hydraulic Conf., Shiraz University, Shiraz. , 1025-1032 Dastorani, M. T. (2007). Evaluation of the application of artificial intelligence models on simulation and real-time prediction of peak flaw. J. of Science and Tech. of Agriculture and Natural Resources (Water and Soil Science). 40, 27-36 Noorani, V., and Salehi, K. (2008). Rainfall-runoff modeling using ANFIS and comparing with ANN and fuzzy logic. 4th National Civil Eng. Conf., Tehran University, Tehran. Goali, Q., Chen, S., and Wang, D. (2001). An intelligent runoff forecasting method based on fuzzy sets, neural network and genetic algorithm. < www.sciencedirect.com.> (Oct. 5, 2009). Chang, F., and Chen, Y.C. (2001). A counter propagation fuzzy-neural network modeling appeoach to real time streamflow prediction.. J. Hydrology. 245, 153-164 Keskin, M.E., Taylan, E.D., and Yilmaz, A.G. (2003). Flow prediction with fuzzy logic appriaches: Dim stream. International Congress on River Basin Management, Antalya, Turkey. Matreata, M. (2004). Artificial nearal network and fuzzy logic moddels in operational hydrological forecasting system. (Sep. 17, 2009). Navak, P. C., Sudheer, K. P., and Ramasastri, K. S. (2004). Fuzzy computing based rainfall-runoff model for real time flood forecasting. J. of Hydrological Proc.. 19, 955-968 Aqil, M., Kita, I., Yano, A., and Nishiyama, S. (2007). A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J. Hydrology. 337, 22-34