Comparison between Genetic Programming and Support Vector Machine Methods for Daily River Flow Forecasting (Case Study: Barandoozchay River)

آب و خاک

Article 6, Volume 28, Issue 6, January 2015, Pages 1162-1171   

Document Type: Research Article

DOI: 10.22067/jsw.v0i0.32406

Authors

F. Ahmadi^{* 1}; F. Radmanesh¹; Rasoul Mirabbasi²

¹Shahid Chamran University

²Shahrekord University

Abstract

Accurate estimation of river flow can have a significant importance in water resources management. In this study, Genetic programming (GP) and Support Vector Machine (SVM) methods were used to forecast daily discharge of Barandoozchay River. The daily discharge data of Barandoozchay River measured at the Dizaj hydrometric station during 2007 to 2011 was used for modeling, which 80% of the data used for training and remaining 20% used for testing of models. The results showed that in the both of considered methods, the models including discharges of one, two and three days ago had higher accuracy in verification step and the accuracy of models decreased with increasing discharge values. Comparing the performance of GP and SVM methods indicated that, however the accuracy of the GP method with the R=0.978 and RMSE=1.66 (m3/s) was slightly more than SVM method with R=0.976 and RMSE=1.80 (m3/s), but the SVM is easier than GP method. Thus, the SVM method can be used as an alternative method in forecasting daily river discharge.

Keywords

Genetic programming (GP); Support Vector Machine (SVM); Daily discharge forecasting; Barandoozchay River

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