Volume 7, Issue 13 (7-2016)                   jwmr 2016, 7(13): 103-92 | Back to browse issues page


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(2016). Comparison of the Performance of Support Vector Machine with other Intelligent Techniques to Simulate Rainfall-Runoff Process. jwmr. 7(13), 103-92. doi:10.18869/acadpub.jwmr.7.13.103
URL: http://jwmr.sanru.ac.ir/article-1-662-en.html
Abstract:   (4434 Views)

     Simulation of rainfall-runoff process is a major step in water engineering studies and water resources management. In this study, the rainfall-runoff process of the Siminehroud monthly (1377-1390) were simulated using Support Vector Machines (SVM)  with Radial Basis kernel Function, Polynomial and linear Bayesian Network (BN) with a PC Learning Algorithm, also conventional methods such as Artificial Neural Networks (ANNs) and Gene Expression Programming (GEP) were used; finally, the results were compared with each other. Correlation Coefficient (CC), Root Mean Square Error (RMSE) and Nash-Sutcliff coefficient (NS) were used to evaluate the performance of the models. The results indicate the acceptable performance of the models and GEP model shows the highest CC (CC = 0.91), minimum RMSE (RMSE = 1.3 m3/s) and NS = 0.82 in verification stage.

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Type of Study: Research | Subject: Special
Received: 2016/07/17 | Revised: 2016/08/30 | Accepted: 2016/07/17 | Published: 2016/07/17

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