assessing the impact of input variables preprocessing into support vector machine through gamma test method for suspended sediment volume prediction

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.