Abstract—Support Vector Machine (SVM) is a kind of learning machine for simulation or prediction. In this paper, Support Vector Machine (SVM) is used to forecast daily river flow and the results of these models are compared with observed daily values. Daily river flow data On Ghara-soo river in north of Iran are used in this study. The daily flow and rain data of station on Ghara-soo as exit discharge and three station of this Catchment Names: shast kalate, ziyarat and Kurd kuy are used to train and test the developed models. The observed data that are used in this study start from 1992 to 2010 in 18 year’s period (6550 days). 75% of the whole data set are used for training the models and 25% of the whole data set are used for testing step. In this regard, five kind of different input data that affect the river flow has been identified and based on this method, the river flow is predicted. In this research, predicted data are compared with actual data through the RMSE index. For checking of proposed model performance in river flow forecasting, the information of Ghara-soo River has been used. The comparison shows that the proposed method yields a high accuracy in the prediction.
Index Terms—Daily discharge, support vector machine (SVM), forecasting, GHARASOO river
M. Moharrampour is with the Department of civil Engineering, Islamic Azad University, Buinzahra branch, Iran (e-mail: M62.firstname.lastname@example.org).
M. Katouzi is with the Young researchers club, Islamic Azad University buinzahra Branch, Iran (e-mail: email@example.com).
Abdulhamid Mehrabi is with Mapna Group, Mapna md1, Guarantee manager, Chabahar, Iran (e-mail: firstname.lastname@example.org)
Cite: Mahdi Moharrampour, Abdulhamid Mehrabi, and Mahya Katouzi, "Daily Discharge Forecasting Using Support Vector Machine," International Journal of Information and Electronics Engineering vol. 2, no. 5, pp. 769-772, 2012.