Application of Wavelet Support Vector machine (WSVM) model in Predicting River Flow (Case study: Dez basin)

River flow prediction is one of the key issues in the management and planning of water resources, in particular the adoption of proper decisions in the event of floods and droughts. To predict the flow rate of rivers, various approaches have been introduced in hydrology, the most important of which are the intelligent models. In this study, a hybrid, model wavelet- support vector machine, was applied to estimate the discharge of Dez river basin based on the daily discharge statistics provided by the hydrometric stations located at the upstream of the dam during the statistical period (2008-2018) and its performance was compared with the support vector machine model. The correlation coefficients, root mean square error, and mean absolute error was used for evaluation and a comparison of the performance of models. The results showed that the hybrid structures presented acceptable outcomes in the modeling of river discharge. A comparison of models also showed that the hybrid model of wavelet -support vector machine has a better performance in forecasting the flow. In conclusion, the use of the WSVM model could be effective in estimating flood peak discharge.