Hybrid Chaotic Genetic Algorithms for Optimal Parameter Estimation of Muskingum Flood Routing Model

Accurate flood routing is essential for flood control in water resources planning and management. The Muskingum model continues to be popular method for flood routing. Its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. In order to improve the flood routing precision, a hybrid chaotic genetic algorithm (HCGA) based on chaotic sequence and GA is proposed for parameter estimation of Muskingum model. Empirical results that involve historical data from existed paper reveal the proposed HCGA outperforms other approaches in the literature.