Estimation of runoff using optimization technique

Over the years, several attempts have been made by the hydrologists to understand transformation of rainfall into runoff in order to forecast streamflow for water supply, irrigation, drainage, power generation and flood control. The rainfall-runoff relationships are among the most intricate hydrologic phenomena to comprehend due to the spatial and temporal variability of watershed characteristics, precipitation patterns as well as a number of variables involved in modelling this physical process. Further, there is a problem of paucity of data which compels hydrologists to adopt simple correlation for adequate estimation of runoff. The accuracy of estimated runoff largely depends upon the accuracy of method adopted to establish the rainfall runoff relationship. The Generalized Reduced Gradient (GRG) technique is reported to be a powerful tool for estimating parameters of linear as well as nonlinear equations and it has, therefore, been implemented to establish the rainfall runoff relationship in the present paper. Earlier, Genetic algorithm (GA) has been successfully applied by several researchers for modelling simple rainfall runoff relationship. The comparative analysis of GRG with GA on the basis of root mean square error (RMSE), Index of Agreement (IA) and Correlation coefficient reveals that GRG technique is as efficient as GA for modelling rainfall runoff relationship. However, the simplicity of GRG technique could prove as an additional advantage over GA. In the present study rainfall runoff data of Gandhi Sagar dam has been utilized.