Optimal Parameter Estimation for Muskingum Model Using a Modified Particle Swarm Algorithm

The accurate parameter estimation for Muskingum model is to be useful to give the flood forecasting for flood control in water resources planning and management. Although some methods have been used to estimate the parameters for Muskingum model, an efficient method for parameter estimation in the calibration process is still lacking. In order to reduce the computational amount and improve the computational precision for parameter estimation, a modified particle swarm algorithm (MPSO) is presented for parameter optimization of Muskingum model. The technique found the best parameter values compared to previous results in terms of the sum of least residual absolute value. Empirical results that involve historical data from existed paper reveal the proposed MPSO outperforms other approaches in the literature.