Abstract
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.
Published Version
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