Optimal Parameter Estimation for Muskingum Model Using a CSS-PSO Method

Abstract EN

Limited availability of hydrologic data is a major hurdle for implementation of detailed hydrologic models.

In cases where available data is limited, simple hydrologic models such as linear Muskingum model consisting of a minimum number (one or two) of model parameters are more desirable.

As an alternative to the conventional mathematical approaches, this paper applies a new hybrid metaheuristic algorithm based on charged system search and particle swarm optimization for identifying the parameters of the linear Muskingum model.

In order to evaluate the new algorithm, a numerical example is utilized and the results are compared to those of other algorithms.

The results reveal the performance of the algorithm to optimize parameter estimation of the Muskingum model.