Document Type: Original Article


1 MSc Student, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Iran

2 Assistant Professor, Faculty of Water Sciences, Shahid Chamran University of Ahvaz, Iran

3 Associate professor, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran


Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran). For this purpose, original time series using wavelet theory decomposed to multi time sub-signals, then these decomposed sub-signals as in input data are used in Adaptive Neural Fuzzy Interference System (ANFIS) and Artificial Neural Network (ANN) for monthly flow prediction. The obtained result shows that WANFIS model has better performance than WANN and can be used for short term and long term flow prediction. One of the weaknesses of fuzzy models is the model estimation error in minimum and maximum points. Which this problem can solve by using hybrid models of wavelet - fuzzy inference system.Also based on results of hybrid model of wavelet- network, it can be concluded that to achieve accurate estimation of the number of different intermediate layers are examined and using one intermediate layer in all conditions is not enough to achieve the best results. Generally, hybrid model of wavelet - Adaptive Neural Fuzzy Interference System have better performance in estimation of the extent points and it is better method for prediction of Gamasyab River flow.


 Adamowski J, Sun K (2010). Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology 390: 85-91.
 Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S (2013). A New Hybrid Artificial Neural Networks for Rainfall–Runoff Process Modeling. Neurocomputing: 05-23.
 Chua LHC, Wong TSW (2010). Improving event-based rainfall–runoff modeling using a combined artificial neural network–kinematic wave approach. Journal of Hydrology 390(1–2): 92-107.
 Jang JSR, Sun CT, Mizutani E (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice-Hall International.New Jersey.
 Kisi O (2008). Stream flow forecasting using neuro-wavelet technique. Hydrological Processes 22: 4142–4152.
 Kişi Ö (2009). Evolutionary fuzzy models for river suspended sediment concentration estimation. Journal of Hydrology 372(1–4): 68-79.
 Komasi M (2007). Modelling rainfall - runoff model using a combination of wavelet - ANN. Ms, Tabriz.
 Mallat S G (1998). A wavelet tour of signal processing, San Diego.
 Mandeville AN, O'Connell PE,Sutcliffe JV, Nash JE (1970). River flow forecasting through conceptual models part III - The Ray catchment at Grendon Underwood. Journal of Hydrology 11(2): 109-128.
 Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004). A neuro-fuzzy computing technique for modeling hydrological time series. Journal of
Hydrology 291(1–2): 52-66.
 Nourani V, Kisi Ö, Komasi M (2011). Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process. Journal of Hydrology 402: 41–59.
 Nourani V, Komasi M (2013). A geomorphology-based ANFIS model for multistation modeling of rainfall–runoff process. Journal of Hydrology 490(0): 41-55.
 Nourani V, Komasi M, Mano A (2009). A Multivariate ANN-Wavelet Approach for Rainfall–Runoff Modeling. Water Resour Manage 23:2877-2894.
 Nourani V, Parhizkar M (2013). Conjunction of SOM-based feature extraction method and hybrid wavelet-ANN approach for rainfall–runoff modeling. Journal of Hydro-informatics 15.3: 829-848.
 Riad S, Mania J, Bouchaou L, Najjar Y (2004). Rainfall-runoff model usingan artificial neural network approach. Mathematical and Computer Modelling 40(7–8): 839-846.
 Talei A, Chua LHC, Quek C, Jansson PE (2013). Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. Journal of Hydrology 488(0): 17-32.