Aplication of the Hybrid Model of Support Vector Machine-Algorithm Artificial Flora in Estimating the Daily Flow of Rivers (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 artificial flora- support vector machine model was applied to estimate the discharge of Dez 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 wavelet-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 correlation coefficient (R= 0.933-0.985), root-mean-square error (RMSE = 0.008-0.088 m3/s), mean absolute error (MAE=0.008-0.088 m3/s) and the Nash–Sutcliffe coefficient (NS=0.951-0.995) has had better performance in estimating the flow. The results of the study of the charts disclosed that the suggested hybrid model has a suitable performance in estimating the minimum and maximum points and has fewer error in all selected stations.