Forecasting daily river flow using an artificial flora–support vector machine hybrid modeling approach (case study: Karkheh Catchment, Iran)

Abstract

In this study, a hybrid support vector machine–artificial flora algorithm method was developed to estimate the flow rate of Karkheh Catchment rivers using daily discharge statistics. The results were compared with those of the support vector–wave vector machine model. The daily discharge statistics were taken from hydrometric stations located upstream of the dam in the statistical period 2008 to 2018. Necessary criteria including coefficient of determination, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Nash–Sutcliffe coefficient were used to evaluate and compare the models. The results illustrated that the combined structures provided acceptable results in terms of river flow modeling. Also, a comparison of the models based on the evaluation criteria and Taylor’s diagram demonstrated that the proposed hybrid method with the correlation coefficient R2= 0.924-0.974, root-mean-square error RMSE= 0.022-0.066 m3/s, mean absolute error MAE= 0.011-0.034 m3/s, and Nash-Sutcliffe coefficient NS=0.947-0.986 outperformed other methods in terms of estimating the daily flow rates of the rivers.

Keywords

• artificial flora
• support vector machine
• wavelet
• karkheh catchment