Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization

Abstract

The Muskingum model is a popular method for flood routing in river engineering. This model has several parameters, which should be estimated. Most of the techniques have applied to estimate these parameters to reduce the distance between observed flow and estimated flows. In this paper, for the first time, the parameters of a novel form of the nonlinear Muskingum model are estimated by the Particle Swarm Optimization (PSO) algorithm. The new Muskingum model, which have four parameters, is applied for three benchmark examples and one real case in Iran. The sum of the squared (SSQ) or absolute (SAD) deviations between the observed and estimated outflows was considered as objective functions. The results showed that although the new Muskingum model became more complex but this model by using PSO technique can improve the fit to observed flow especially in multiple-peak hydrographs.