Parameter Estimation of Muskingum Model based on Whale Optimization Algorithm with Elite Opposition-based Learning

Abstract

In order to solve the problem of complexity and poor accuracy for parameter estimation of Muskingum model, the Whale Optimization Algorithm with Elite Opposition- based Learning (EOWOA) was established to estimate the parameters of Muskingum model. EOWOA used the elite opposition-based learning to improve the whole search capability of WOA, and the evolution process can be quickened, moreover the convergence speed and accuracy was also improved. And the benchmark function tests demonstrated that EOWOA outperforms DE, PSO and WOA. Finally, the results of application showed that IWOA can effectively estimate the parameters of Muskingum model, and the precision of this method wins great satisfaction, thus to provide a new way in the field of channel flood routing.