Hybrid Chaotic Genetic Algorithms for Optimal Parameter Estimation of Muskingum Flood Routing Model

Author

Wang, Wenchuan ; Xu, Zhengmin ; Qiu, Lin ; Xu, Dongmei

Author_Institution

North China Inst. of Water Conservancy & Hydroelectric Power, Zhengzhou, China

Volume

1

fYear

2009

fDate

24-26 April 2009

Firstpage

215

Lastpage

218

Abstract

Accurate flood routing is essential for flood control in water resources planning and management. The Muskingum model continues to be popular method for flood routing. Its parameter estimation is a global optimization problem with the main objective to find a set of optimal model parameter values that attains a best fit between observed and computed flow. In order to improve the flood routing precision, a hybrid chaotic genetic algorithm (HCGA) based on chaotic sequence and GA is proposed for parameter estimation of Muskingum model. Empirical results that involve historical data from existed paper reveal the proposed HCGA outperforms other approaches in the literature.

Keywords

environmental management; floods; parameter estimation; water resources; Muskingum flood routing model; global optimization problem; hybrid chaotic genetic algorithms; optimal parameter estimation; water resources management; water resources planning; Ant colony optimization; Biological system modeling; Chaos; Floods; Genetic algorithms; Genetic mutations; Parameter estimation; Rivers; Routing; Water resources;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on

Conference_Location

Sanya, Hainan

Print_ISBN

978-0-7695-3605-7

Type

conf

DOI

10.1109/CSO.2009.34

Filename

5193678