Stage-Discharge Relationship Modeling in River Using Artificial Neural Network (ANN) and Group Method of Data Handling (GMDH) Methods (Case study: Schuylkill River)

River flow forecasting in rivers, is one of the most important components of hydraulic and hydrological processes in water resource management. For various hydrological applications such as water and sediment budget analysis, operation and control of water resources projects, the accurate information about flow value in rivers is very important. For this reason hydrologists use historical data to establish a function relationship between water level and discharge which is known stage-discharge relation or rating curve (RC). With the recent advancements in artificial intelligence and soft computing in water resource studies, there is a choice of better techniques to modeling hydraulic and hydrological processes. This paper describes the use of the Group Method of Data Handling (GMDH) that is a self organization modeling approach base on data, for stage-discharge relationship modeling at Philadelphia station in Schuylkill River, USA. 12 Different input combinations including the previous stages and discharges with 2 kind of active function are used. The accuracy of model was evaluated using Root Mean Square Error (RMSE), Mean Percent Relative Error (MPRE) and Nash–Sutcliffe model efficiency coefficient (NASH). The values of statistical parameters RMSE, MPRE and NASH for the best model of stage- discharge relationship in this river in validation period are 15.8, 0.303 and 0.999 respectively.