Parameter Estimation of the Nonlinear Muskingum Flood-Routing Model Using Water Cycle Algorithm

Abstract

Flood routing in river is one of important issues in water engineering projects. Hydraulic routing is common in especially in river that has branches and river that have not basin information. So as to need obtain cross section and slops in all interval of river that Muskingum helps by saving time and cost. In this paper, a Water Cycle Algorithm (WCA) is proposed for the parameter estimation of the nonlinear Muskingum model. The results of the developed model were compared with those of the other meta­heuristic algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Harmony Search Algorithm (HS) and Imperialist Competitive Algorithm (ICA). In the proposed technique, an indirect penalty function approach is imposed on the model to prevent negativity of outflows and storages. The proposed algorithm finds the global or near-global minimum regardless of the initial parameter values with fast convergence. The proposed algorithm found the best solution among 5 different methods. The results demonstrate that the proposed algorithm can be applied confidently to estimate optimal parameter values of the nonlinear Muskingum model. Moreover, this algorithm may be applicable to any continuous engineering optimization problems.