Comparison of the performances of the gene expression programming model and the RegCM model in predicting monthly runoff

Abstract Prediction of rainfall and runoff is one of the most important issues in managing catchment water resources and sustainable use of water resources. In this study, the accuracy and efficiency of the Gene Expression Programming (GEP) model and the Regional Climate Model (RegCM) to predict runoff values from monthly precipitation were investigated. For this purpose, monthly precipitation data of 48 synoptic stations, monthly temperature data of 21 synoptic stations, and also monthly runoff data of 40 hydrometric stations located in the Karkheh basin during 45 years (1972–2017) were used. Out of this statistical period, 40 years was used for calibration, and five years (1995–1999) for the validation of the model results. The results showed that the GEP model with an average R2 value of 0.948, average RMSE value of 19.4 m3/s, average NSE value of 0.91, and average SE value of 0.3, had a much more accurate performance than the RegCM model, which had an average R2 value of 0.04, average RMSE value of 298.2 m3/s, average NSE value of −0.64, and average SE value of 4.6 in predicting monthly runoff.