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


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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
  • Publish Date: 22 September 2016