Closure to 'Applying Particle Swarm Optimization to Parameter Estimation of the Nonlinear Muskingum Model' by H.-J. Chu and L.-C. Chang

摘要

Before obtaining optimal parameters in the nonlinear Muskingum model, model uncertainty is the core of the problem, especially model structure uncertainty. The GLUE technique allowed assessment of the uncertainty presented in various model parameters. Result proved that parameter estimation problem is comprehensive and extensive. It is necessary to consider not only accuracy of the optimization method but also the simulation model accuracy. Eventually, our study suggested that an optimization technique such as PSO is another way to obtain the near-optimal parameters of the nonlinear Muskingum model.