Estimation of the type 5 Muskingum nonlinear model parameters in the flood routing with The Harris Hawks Optimization Algorithm (HHO)

One of the major issues in the hydrology is the prediction of flooding and subsidence, or the rise and fall of river hydrographs at a certain point. This can be analyzed by flood routing. The Muskingum method is one of the hydrological methods that can be used to save time and money with simple operation and proper accuracy of flood routing. The use of meta-heuristic methods has shown satisfactory results so far. Therefore, in this study, the performance of Harris hawks optimization (HHO) algorithm in estimating the optimal parameters of the non-linear Muskingum model has been evaluated. In this paper, the fifth type of Muskingum nonlinear model (NL5) was first used to evaluate the HHO algorithm in the Wilson River (Applied Research) and the Karun River (Case Study). In order to evaluate the desirability of the research findings, the results were compared to the results of the genetic algorithms (GA) and harmonic search (HS). The results of the HHO algorithm for both the Wilson and Karun rivers indicate the minimization of the sum of squares (SSQ) as the objective function, which is 11.64 for the Wilson River and 143050.02 for the Karun River. Based on the results, the HHO has better performance than the GA and HS algorithms, so the proposed algorithm can be used with good confidence to estimate the optimal values of nonlinear Muskingum model parameters.