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A reliable model for the prediction of rainfall-runoff process is important for water resource planning and management. The accurate estimation of monthly rainfall and runoff also can be considered a key element for predicting drought in the near future. Rainfall is the most important factor for flow. Therefore, knowing the amount of rainfall in a basin can help to improve the accuracy of forecasting models for river flow Precipitation and flow have a complex and nonlinear relationship. Additionally, the effect of precipitation on the flow depends on basin characteristics such as topography, shape, slope, type of soil, soil moisture, temperature and evaporation. These parameters are generally considered in conceptual models. However, since the basin characteristics are difficult to determine, in recent years the relationship between precipitation and flow can be made with limited data by artificial intelligence methods. In this study, monthly mean flow values of E21A057 Stream Gauging Station (SGS) are estimated by Genexpression Programming (GEP) and Multiple Linear Regression (MLR) methods by using monthly total precipitation values of 17204 meteorological observation station. Models which are prepared using different input parameters, the GEP method is found to be more successful in almost all cases compared to the MLR method. The most successful model among all models is determined as DM04 using four input parameters in GEP method. These parameters are Precipitation (P), 1 month lagged flow rate (Q-1), 1 month lagged precipitation (P-1) and 2 month lagged precipitation (P-2).

Evaluation of Gene Expression Method for Rainfall-Runoff Relationship: Case study of the E21A057 Station