Volume 25, Issue 1 (Spring 2021)                   jwss 2021, 25(1): 43-62 | Back to browse issues page

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Alinezhadi M, Mousavi S F, Hosseini K. Comparison of Gene Expression Programming (GEP) and Parametric and Non-parametric Regression Methods in the Prediction of the Mean Daily Discharge of Karun River (A case Study: Mollasani Hydrometric Station). jwss 2021; 25 (1) :43-62
URL: http://jstnar.iut.ac.ir/article-1-3999-en.html
1- Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran. , mehdi_alinejadi@semnan.ac.ir
Abstract:   (2172 Views)
Nowadays, the prediction of river discharge is one of the important issues in hydrology and water resources; the results of daily river discharge pattern could be used in the management of water resources and hydraulic structures and flood prediction. In this research, Gene Expression Programming (GEP), parametric Linear Regression (LR), parametric Nonlinear Regression (NLR) and non-parametric K- Nearest Neighbor (K-NN) were used to predict the average daily discharge of Karun River in Mollasani hydrometric station for the statistical period of 1967-2017. Different combinations of the recorded data were used as the input pattern to predict the mean daily river discharge. The obtained esults  indicated that GEP, with R2= 0.827, RMSE= 59.45 and MAE= 26.64, had a  better performance, as compared to LR, NLR and K-NN methods, at the  validation stage for daily Karun River discharge prediction with 5-day lag, at the Mollasani station. Also, the performance of the models in the maximum discharge prediction showed that all models underestimated the flow discharge in most cases. 
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Type of Study: Research | Subject: Ggeneral
Received: 2020/03/7 | Accepted: 2020/08/5 | Published: 2021/05/31

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