Predictive modelling of the stage-discharge relationship using Gene-Expression Programming

Modelling the hydrologic processes is an essential tool for the efficient management of water resource systems. Therefore, researchers are consistently developing and improving various predictive/forecasting techniques to accurately represent a river's attributes, even though traditional methods are available. This paper presents the Gene-Expression Programming (GEP) modelling technique to accurately model the stage-discharge relationship for the Arouca River in Trinidad and Tobago using only low flow data. The proposed method uses the stage and associated discharge measurements at one cross-section of the Arouca River. These measurements were used to train the GEP model. The results of the GEP model were also compared to the traditional method of the Stage-Discharge Rating Curve (SRC). Four statistical paraments namely the Pearson's Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Relative Error (MARE) and Nash-Sutcliffe efficiency (NSE) were used to evaluate the performance of the GEP model and the SRC method. Overall, the GEP model performed exceptionally well with an R-2 of 0.990, RMSE of 0.104, MARE of 0.076 and NSE of 0.957.