In this study, the hybrid support vector machine-artificial flora algorithm method was developed and the results were compared with those of the support vector machine-wavelet model. The case study of Dez catchment area was used in order to estimate the flow rate of the rivers employing the daily discharge statistics from hydrometric stations located upstream of the dam in the statistical period from 2008 to 2018. The criteria of coefficient of determination, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and NashSutcliffe coefficient were used to evaluate and compare the models. The results showed that the combined structures provided acceptable results in river flow modeling. Also, comparison of the models based on the evaluation criteria and Taylor’ ؛ s diagram showed that the proposed hybrid support vector machine – ؛ artificial flora with the correlation coefficient (R 2 = 0. 933-0. 985), root-mean-square error (RMSE= 0. 008-0. 088 m 3 /s), mean absolute error (MAE= 0. 004-0. 040 m 3 /s), and Nash-Satcliffe coefficient (NS=0. 951-0. 995) performed better in estimating the daily flow rates of rivers.