University of Tehran Journal of Range and Watershed Managment 5044-2008 67 2 2014 06 22 Assessing the Impact of input variables preprocessing into support vector machine through gamma test method for suspended sediment volume prediction Assessing the Impact of input variables preprocessing into support vector machine through gamma test method for suspended sediment volume prediction 289 303 51833 10.22059/jrwm.2014.51833 FA Elham Kakaei Lafdani PhD Candidate, Watershed Management Engineering, Faculty of Natural Resources, Tarbiat Modares University, Noor, Iran Ali Reza Moghaddam Nia Associate Professor, Mountain and Arid Regions Reclamation Department, Faculty of Natural Resources, University of Tehran, Karaj, Iran Azadeh Ahmadi Assistant Professor, Department of Civil Engineering, Isfahan University of Technology, Isfahan, Iran Heydar Ebrahimi PhD Candidate, Watershed Management Engineering, Department of Watershed Management, University of Kashan, Kashan, Iran Journal Article 2012 11 18 This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables and sediment discharge as the output model. Also, the duration of the model training period was determined through GT. Thereafter, in order to carry out the influence of pre-processing input variables on performance of model, the suspended sediment was predicted using SVM model while no pre-processing has been done on its input variables and the results were compared to each other. Results show the performance of the GT-SVM model in the test phase with minimum RMSE was equal to 0.96 (ton/day) and the maximum coefficient of R² was equal to 0.98 between the predicted and actual values, was better than SVM model. This study aimed to examine the influence of pre-processing input variables by Gamma Test on performance of Support Vector Machine in order to predict the suspended sediment amount of Doiraj River, located in Ilam Province from 1994-2004. The flow discharge and rainfall were considered as the input variables and sediment discharge as the output model. Also, the duration of the model training period was determined through GT. Thereafter, in order to carry out the influence of pre-processing input variables on performance of model, the suspended sediment was predicted using SVM model while no pre-processing has been done on its input variables and the results were compared to each other. Results show the performance of the GT-SVM model in the test phase with minimum RMSE was equal to 0.96 (ton/day) and the maximum coefficient of R² was equal to 0.98 between the predicted and actual values, was better than SVM model. https://jrwm.ut.ac.ir/article_51833_ec128ccc8b06ac11f4b978acfc1834c2.pdf