Comparison of different objective function on estimation of linear and non-linear Muskingum model optimum parameters

Prediction and control flood is essential in water resource management. Studying and analysis on this problem is done using flood routing methods in hydraulic science. The Muskingum model is one of the best methods in the hydrologic flood routing rivers. In order to estimate of the Muskingum parameters have been used experiential methods as trial and error method، least squares. Genetic algorithm is one of the estimation methods of the parameters. Comparison between experiential methods results and genetic algorithm show that Genetic algorithm method is benefit and easy method for estimated the Muskingum parameters. The determining of objective function is most important in this method (GA). In this paper، different objective functions have been selected for linear and non-linear Muskingum models and are optimized using genetic algorithm then the effects of objective function are investigated on prediction of Muskingum parameters and computational output discharge. Comparison between computational hydrograph of output discharge and experimental result show that the effects of objective function on result prediction predominate in the linear Muskingum model whereas these are virtually negligible in non-linear Muskingum model.