Development of Nonlinear Muskingum Model Using Evolutionary Algorithms Hybrid

The nonlinear Muskingum model has a significant advantage as compared to the linear one due to the nonlinear relationship between storage and flow dishcrage. In this model, the correct estimation of the parameters is necessary to achieve the proper precision. Previous studies indicate that there are five nonlinear corrected models, which, with different optimization algorithms, tried to increase the prediction accuracy of output hydrographs. Due to the error in the output hydrograph of the previous models, in this study, a new structure of nonlinear Muskingum model was developed based on hybrid PSO and DSO algorithms. In this model (NL6 model) with eight parameters, the improvement coefficient γ are used. This coefficient takes less and more than one according to the number of peak discharge in the output hydrograph. By applying the proposed approach to the three types of input hydrograph and determining the optimal values of the parameters of the NL6 model, it shows that this model has a high accuracy in estimating the discharge values of the output hydrograph. The error reduction rate of the NL6 model based on SSQ and SAD indicators for multi-peak hydrographs is 53 and 35.6 percent compared to the last proposed model, respectively. So, this model can have a high performance in estimated flood routing hydrograph.