Parameter Estimation of Two Improved Nonlinear Muskingum Models Considering the Lateral Flow Using a Hybrid Algorithm

• Published in: Water resources management Vol. 31; no. 14; pp. 4449 - 4467
• Main Authors: Kang, Ling, Zhou, Liwei, Zhang, Song
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2017; Springer Nature B.V
• Subjects: Adaptive algorithms; Algorithms; Atmospheric Sciences; Civil Engineering; Earth and Environmental Science; Earth Sciences; engineering; Environment; Flood routing; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Hydrology/Water Resources; Iran; Mathematical models; Parameter estimation; Water resources; Iran; Flood routing; Lateral flow; Variable exponent; Nonlinear Muskingum model; Hydrologic method
• ISSN: 0920-4741; 1573-1650
• DOI: 10.1007/s11269-017-1758-7

The Muskingum model was one of the most popular methods for flood routing in water resources engineering, many researchers had presented various versions of Muskingum model so as to enhance the precision of the Muskingum model in their papers. Similarly, two new nonlinear Muskingum models were presented in this paper. One considered the lateral flow, and the other considered the lateral flow and a variable exponent parameter, simultaneously. Minimizing the sum of the squared (SSQ) deviations between the observed and routed outflows was considered as the objective, and then three benchmark examples and a real example in Iran were applied to verify performances of two proposed models. A hybrid algorithm, which combined the improved real-coded adaptive genetic algorithm and the Nelder-Mead simplex algorithm, was utilized for parameter estimation of two proposed models. Comparisons of the optimal results for four examples by different models showed that two proposed models can produce more accurate fit to observed outflows, and the proposed model, which simultaneously considered a variable exponent parameter and the lateral flow, reduced the SSQ obviously.