Parameter Estimation for Nonlinear Muskingum Model Based on Immune Clonal Selection Algorithm

Abstract

Parameter estimation of the nonlinear Muskingum model is a highly nonlinear optimization problem. Although various techniques have been applied to estimate the parameter of the nonlinear Muskingum flood routing model, an efficient method for parameter estimation in the calibration process is still lacking. In this paper, a novel approach of parameter estimation for the nonlinear Muskingum model based on the immune clonal selection algorithm (ICSA) is proposed. ICSA is a new intelligent algorithm, which can effectively overcome the prematurity and slow convergence speed of the traditional evolution algorithm. The ICSA method does not demand any initial estimate of values of any of the parameters. It determines the best parameter values in terms of the sum of square residual between the observed and routed outflows. The performance of this method was compared with other reported parameter estimation approaches. The results indicate that the ICSA method had higher precision than the other techniques and thus provided an efficient way for parameter estimation of the nonlinear Muskingum model.