Application of the nonlinear Muskingum model in flood routing of Doab Samsami River

Flood is one of the most damaging natural disasters that causes many damages every year throughout the world. Hence, the ability to estimate floods in each region can play an important role in preventing the possible damages caused by this phenomenon. Nonlinear Muskingum model is a hydrological flood routing method, which has shown an appropriate performance. However, its acceptable performance relies on the optimized estimates of three parameters used in the method. Therefore, in this study, in order to flood routing of two flood events recorded in the Doab Samsami River the nonlinear Muskingum model was applied. In order to estimate parameter models, two evolutionary algorithms were used including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results of study showed that both GA and PSO methods provided very similar outputs to find the desired optimal parameters. Using the Optimal parameters obtained from mentioned algorithms, flood routing was carried out. In both floods, nonlinear Muskingum model depicted an appropriate performance so that in estimating the peak discharges, there were no error in terms of peak discharge time, and considering peak discharge amount there were slight differences. As an example, in the case of the second flood, the maximum error in estimating the peak discharge was equal to 0.1 cubic meters per second.

Keywords:

Flood Routing , Nonlinear Muskingum Model , Evolutionary Algorithms , Doab Samsami River