Parameters Estimation of the Nonlinear Muskingum Flood-Routing Model Using Wolf Search Algorithm (WSA) (Case Study: Kardeh River)

Flood routing is one of the most complex problems that is encountered in open channel hydraulics and river engineering. Among the different flood routing methods, the Muskingum model, as the most applied hydrologic method of flood routing, has been widely used with high accuracy in river flood studies. The parameters estimation of the nonlinear Muskingum flood-routing model has been considered by different researchers. In this paper, the wolf search algorithm (WSA) was used to this for assessing the optimum values of Muskingum parameters, the objective function was defined as the minimizing of the sum of square residuals between the observed and routed outflows. To investigate the suitability of research findings, the results of the WSA were compared with other heuristic algorithms including genetic algorithm (GA), particle swarm optimization (PSO), harmony search (HS), and imperialist competitive algorithm (ICA). Six benchmark functions with different dimensions were used to evaluate the capability of algorithms. The results showed that the WSA is capable of providing satisfactory estimates of nonlinear Muskingum parameters. The obtained values of R2 and RMSE were 0.99261 and 2.419886 for Kardeh river and 0.778425 and 0.712358 for Wilson river, respectively.