Parameter estimation of nonlinear Muskingum models using genetic algorithm

The application of the Muskingum model to river and channel flood routing may have some limitations because of its inherent assumption of a linear relationship between channel storage and weighted flow. Although nonlinear forms of the Muskingum model have been proposed, an efficient method for parameter estimation in the calibration process is still lacking. In this paper, the objective approach of genetic algorithm is proposed for the purpose of estimating the parameters of two nonlinear Muskingum routing models. The performance of this algorithm is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the genetic algorithm approach is efficient in estimating parameters of the nonlinear routing models. A supplementary analysis of the sensitivity of the parameters during the performance of genetic algorithm shows that a unique set of parameters exists that would result in the best performance for a given problem. In addition, genetic algorithm does not demand any initial estimate of values of any of the parameters, and thus avoids the subjectivity and computation time associated with the traditional estimation methods.