Evaluation the Accuracy of ANFIS, SVM and GP Models to Modeling the River Flow Discharge

Document Type : Research Article


1 Ph.D Student of Water Resources Management, Birjand University, Birjand, Iran.

2 Department of Water Engineering, Birjand University, Birjand

3 Ph.D Student of Water Resources Management, Shahid Chamran University, Ahwaz, Iran.

4 Ph.D Student of Watershed, Kashan University, Kashan, Iran.


Prediction the river flow discharge values are important in the surface water resources management. Find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. In this study three ANFIS, SVM and GP models were evaluated and compared to modeling the monthly flow discharge of Nazloochi River in Tapik hydrometric station that located in western of Urmia Lake based on precipitation of river basin. All the methods listed in M1 to M5 data flow patterns with a delay of 1 to 5 M6 to M10 and patterns of precipitation and discharge data combined with delays of one to five months were studied.To investigate the value of modeling’s error three coefficient of determination, root mean square error and effectiveness criteria tests were used. The results of evaluation the accuracy and error values of models indicated that the combined pattern has better results only in SVM model and in GP and ANFIS models the ones series patterns presented the better results. Among the three studied models, ANFIS model with 4 and 5 delays input patterns presented the best results. Overall the results indicated that with adoption of ANFIS model to modeling the monthly river flow in Nazloochai River, error values were decreased about 23 and 3 percentages respectively in GP and SVM models and accuracy of modeling compared to GP and SVM models were increased about 10 and 4 percent respectively.


Main Subjects


    1. Govindaraju RS. Artificial neural network in hydrology. Journal of hydrologic Engineering. 2000;5(2): 115-123.
    2. Koza JR. Genetic Programming: on the programming of computers by means of natural selection. Cambridge, MA: MIT Press. 1992.
    3. Alvisi S, Mascellani G, Franchini M, Bardossy A. Water level forecasting through fuzzy logic and artificial neural network approaches. J Hydrol Earth Sys Sci. 2005;2:1107-1145.
    4. Aytek A, Kisi O. A genetic programming approach to suspended sediment modeling. Journal of Hydrology. 2008;351: 288-298.
    5. Wang WC, Chau KW, Cheng CT, Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology. 2009;374: 294-306.
    6. Guven, A. Linear genetic programming for time-series modeling of daily flow rate. J Earth Syst. Sci. 2009;118 (2): 157-173.
    7. Ghorbani MA, Kisi O, Aalinezhad, M. A probe into the chaotic nature of daily stream flow time series by correlation dimension and largest Lyapunov methods. Applied Mathematical Modelling. 2012;34: 4050–4057.
    8. Zahiri A, Azamathulla HM. Comparison between linear genetic programming and M5 tree models to predict flow discharge in compound channels. Neural Comput & Applic. 2014;24:413–420.
    9. Vapnik VN. Statistical Learning Theory. Wiley, New York. 1998

    10. Pai PF, Hong WC. A recurrent support vector regression model in rainfall forecasting. Hydrological Process. 2007;21: 819-827.

    11. Hamel LH. Knowledge discovery with support vector machines (Vol. 3). John Wiley & Sons. 2011.

    12. Adamowski J, Prasher SO. Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data. Journal of Water and Land Development. 2012;17(8):89–97.

    13. Choubey V, Mishra S, Pandy SK. Time Series Data Mining in Real Time Surface Runoff Forecasting through Support Vector Machine. International Journal of Computer Applications. 2014;98(3): 23-30.

    14. Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on. 1993; 23(3): 665-685.

    15. Nayak PC, Sudheer KP, Rangan DM, Ramasastre KS. A neuro-fuzzy computing technique for modeling hydrological time series. Journal of Hydrology. 2004;291(1-2):52–66

    16. Sanikhani H. Kisi, O. River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resources Management. 2012;26(6): 1715-1729.

    17. Talei A, Chua LHC, Wong TSW. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall-runoff modeling. Journal of Hydrology. 2010;391(3-4): 248-262.

    18. Ghose D, Panda P, Swain P. Prediction and optimization of runoff via ANFIS and GA. Alexandria Engineering Journal. 2013;52(2): 209-220.

    19. Chen SH, Lin YH, Chang LC, Chang FG. The strategy of building a flood forecast model by neuro fuzzy network. Hydrological Processes. 2006;20(7): 1525- 1540.

    20. He Z, Wen X, Liu H, Du J. A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region. Journal of Hydrology. 2014;509:379–386

    21. Karakus M, Tutmez B, Fuzzy and multiple regression modeling for evaluation of intact rock strength based on point load, Schmidt hammer and sonic velocity, Rock Mech. Rock Eng. 2006;39(1): 45–57.

    22. Jang JSR. Fuzzy controllersbased on temporal back propagation, IEEE Trans. Neural Netw. 1992; 3:714–723.

    23. Ferreira C. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems. Complex Systems, forthcoming. 2001.

    24. Hofmann T, Tsochantaridis I, Altun Y. Learning over structured output spaces via joint kernel functions. Sixth Kernel Workshop. 2002

    25. Eskandari A, Nouri ;R, Meraji H, Kiaghadi A. Developing a Proper Model for Online Estimation of the 5-Day Biochemical Oxygen Demand Based on Artificial Neural Network and Support Vector Machine. J of Environment Studies. 2012;38(1):71-82 [Persian].

    26. Nash JE, Sutcliffe JV. River flow forecasting through conceptual models part I — A discussion of principlesJournal of Hydrology. 1970;10(3): 282–290

    27. Swinscow TDV, Campbell MJ. Statistics at Square One. London: BMJ Publication. 106 P. 2002.

    28. Salas JD, Delleur JW, Yevjevich V, Lane WL. Applied Modeling of Hydrologic Time Series. Water resource Publications, P. O. Box 2841. Littleton, Colorado.80161, U.S.A. 1980; 484 P.

Volume 3, Issue 3
September 2017
Pages 347-361
  • Receive Date: 19 November 2016
  • Revise Date: 11 December 2016
  • Accept Date: 19 December 2016
  • First Publish Date: 19 December 2016