Hybrid particle swarm optimization for parameter estimation of Muskingum model

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• 作者：Aijia Ouyang (1)
  Kenli Li (1)
  Tung Khac Truong (2)
  Ahmed Sallam (3)
  Edwin H.-M. Sha (4)
  
• 关键词：Particle swarm optimization ; Nelder–Mead simplex method ; Muskingum model ; Hybrid algorithm ; Parameter estimation
• 刊名：Neural Computing & Applications
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：25
• 期：7-8
• 页码：1785-1799
• 全文大小：664 KB
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• 作者单位：Aijia Ouyang (1)
  Kenli Li (1)
  Tung Khac Truong (2)
  Ahmed Sallam (3)
  Edwin H.-M. Sha (4)
  
  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, 410082, China
  2. Faculty of Information Technology, Industrial University of Hochiminh City, Hochiminh, Vietnam
  3. Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt
  4. College of Computer Science, Chongqing University, Chongqing, 400044, China
  
• ISSN：1433-3058

文摘

The Muskingum model is the most widely used and efficient method for flood routing in hydrologic engineering; however, the applications of this model still suffer from a lack of an efficient method for parameter estimation. Thus, in this paper, we present a hybrid particle swarm optimization (HPSO) to estimate the Muskingum model parameters by employing PSO hybridized with Nelder–Mead simplex method. The HPSO algorithm does not require initial values for each parameter, which helps to avoid the subjective estimation usually found in traditional estimation methods and to decrease the computation for global optimum search of the parameter values. We have carried out a set of simulation experiments to test the proposed model when applied to a Muskingum model, and we compared the results with eight superior methods. The results show that our scheme can improve the search accuracy and the convergence speed of Muskingum model for flood routing; that is, it has higher precision and faster convergence compared with other techniques.