Identification of water quality model parameters using artificial bee colony algorithm

Guangzhou Chen; Guijian Liu; Jiaquan Wang; Ruzhong Li

doi:10.3934/naco.2012.2.157

Article Contents

2012, Volume 2, Issue 1: 157-165. Doi: 10.3934/naco.2012.2.157

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Identification of water quality model parameters using artificial bee colony algorithm

1.
Department of Environmental Engineering, Anhui University of Architecture, No. 856, JinZhai South Road, Hefei 230022
2.
School of Earth and Space Sciences, University of Science and Technology of China, No. 96, JinZhai Road, Hefei 230026
3.
School of Resources and Environmental Engineering, Hefei University of Technology, No. 193, TunXi Road, Hefei 230009

Received: January 31, 2011

Revised: September 30, 2011

Published: February 2012

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Abstract

As prime data of water quality model, water quality parameters are of importance to forecast the situation of water quality correctly. Therefore, it is a key to identify them correctly. Aimed at the parameter identification problem, it can be transformed into an optimization problem by constructing objective function that minimizes simulation errors. In this study, a novel swarm intelligence optimization algorithm-artificial bee colony algorithm was used. In the experiment, many tests were done under the various ranges of parameters, and each variable was optimized according to its own reasonable scope. In addition, the optimization effect was compared based on the two methods producing new solutions in the neighborhood. As a key parameter of the algorithm, the impact of limit value on the algorithm performance was analyzed in detail under various values. Finally, two examples were analyzed and their computation results were compared with that of artificial fish swarm algorithm, simulated annealing and genetic algorithm. The results show that artificial bee colony algorithm has good adaptability to various ranges of parameters and better optimization precision. Moreover, it needs few control parameters of algorithm. So it is an effective parameter identification method.

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Mathematics Subject Classification: Primary: 65K99; Secondary: 90C31.

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References

[1]	G. Z. Chen, X. C. Xu and J. Q. Wang, Application of a modified artificial fish swarm algorithm to identification of water quality parameters, Journal of Hydroelectric Engineering, 29 (2010), 108-113.
[2]	G. W. Fu, "River Water Quality Model and Simulation Computation," 1^st edition, China Environmental Science Press, Beijing, 1987.
[3]	J. Q. Guo, Y. Li and H. S. Wang, Chaotic optimization for parameter estimation of water quality model of river, Journal of Hydroelectric Engineering, 15 (2004), 95-99.
[4]	J. Q. Guo, Y. Li and H. S. Wang, Application of particle swarm optimization algorithms to determination of water quality parameters of river streams, Advances in Science and Technology of Water Resources, 27 (2007), 1-5.
[5]	F. Kang, J. J. Li and Q. Xu, Improved artificial bee colony algorithm and its application in back analysis, Water Resources and Power, 27 (2009), 126-129.
[6]	D. Karaboga, An idea based on bee swarm for numerical optimization [R], Technical Report-TR06, Turkey: Erciyes University, 2005.
[7]	Y. H. Jia, Determination of water quality parameter based on genetic algorithm, Haihe Water Resources, 1 (2008), 41-42.
[8]	D. Karaboga and B. Basturk, A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, 39 (2007), 459-471.doi: 10.1007/s10898-007-9149-x.
[9]	D. Karaboga and B. Basturk, On the performance of artificial bee colony (ABC) algorithm, Applied Soft Computing, 8 (2008), 687-697.doi: 10.1016/j.asoc.2007.05.007.
[10]	D. Karaboga, Artificial bee colony algorithm, Scholarpedia, 5 (2010), 6915. Available from: http://dx.doi.org/10.4249/scholarpedia.6915doi: 10.4249/scholarpedia.6915.
[11]	L. Q. Meng and J. Q. Guo, Application of chaos particle swarm optimization algorithm to determination of water quality parameter of river steam, Journal of Earth Sciences and Environment, 31 (2009), 169-172.
[12]	J. P. Wang and S. T. Cheng, Application of genetic algorithm and simplex method in parameter identification of complicated environmental model, Journal of Hydraulic Engineering, 36 (2005), 674-679.
[13]	W. Wang, G. M. Zeng and L. He, Estimation of water quality model parameters with simulated annealing algorithm, Shui Li Xue Bao, 6 (2004), 61-67.
[14]	X. H. Yang, Z. F. Yang and J. Q. Li, A new method for parameter identification in water environment model, Advances in Water Science, 14 (2003), 554-557.
[15]	Y. J. Zhang, J. Q. Guo and H. S. Wang, Chaotic-Annealing algorithm for parameter identification of river water quality model, China Rural Water and Hydropower, 1 (2006), 38-41.
[16]	S. Zhu, G. H. Mao and G. H. Liu, Parameters identification of river water quality model based on finite volume method-hybrid genetic algorithm, Journal of Hydroelectric Engineering, 26 (2007), 91-95.