Study of Identifying Parameter of River Flow Model Dynamically Based on the Hybrid Accelerating Genetic Algorithm

In this paper, the real-valued accelerating genetic algorithm, the hybrid accelerating genetic algorithm is proposed for dynamic parameter optimization of river flow model, in which the initial population are generated by chaos algorithm, the chaos mutation operation is used during evolution, and the local search operator is imbedded after evolution iteration. Eight traditional nonlinear test functions are simulated to verify the higher searching efficiency and solution precision compared with standard binary-encoded genetic algorithm and real-valued accelerating genetic algorithm. From viewpoint of realizing the control of river flow kinetic nonlinear system, the hybrid accelerating genetic algorithm and the unsteady river flow model are coupled by time interval in order to dynamically identify the roughness parameter and get the updated state variable values. The flood routing results of the Yangtse reach from Qingxichang to Wanxian show that the fitting precision between the simulated and observed stage is greatly improved, and the roughness reflects the changing characteristics along with flood fluctuating dynamically, thereby the feasibility of identifying the roughness parameter using the hybrid accelerating genetic algorithm is demonstrated.