Water quality prediction using LS-SVM with particle swarm optimization

Proceedings - 2009 2nd International Workshop on Knowledge Discovery and Data Mining, WKKD 2009

Volumn , Issue , 2009, Pages 900-904

  Yunrong, Xiang ^a   Liangzhong, Jiang ^b  

^a Environment Protection Monitoring   (China)

^b SOUTH CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

LS SVM; Particle swarm optimization; Water quality prediction

Indexed keywords

BP ALGORITHM; GUANGZHOU; HIGH EFFICIENCY; LEAST SQUARES SUPPORT VECTOR MACHINES; LS-SVM; MINIMUM VALUE; MULTI LAYER PERCEPTRON; QUALITY PREDICTION; QUALITY PREDICTION MODELS; TIME SERIES PREDICTION; WATER QUALITY PREDICTION;

BACKPROPAGATION ALGORITHMS; CURVE FITTING; HYDRAULIC MODELS; INFORMATION MANAGEMENT; MINING; MULTILAYER NEURAL NETWORKS; RESEARCH AIRCRAFT; SUPPORT VECTOR MACHINES; WATER POLLUTION; WATER QUALITY;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 66249142719     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WKDD.2009.217     Document Type: Conference Paper

References (15)

1. Frauke Friedrichs, Christian Igel, Evolutionary tuning of multiple SVM parameters, Neurocomputing 64 (2005) 107-117.
2. Corinna Cortes and Vladimir Vapnik, Support-Vector Networks, 1995
3. Dong Chunxi, Yang Shaoquan, Research on parameters selection of SVM, System engineering and electronics, vol.26, No. 18, Aug. 2004.
4. Vladimir Cherkassky, Mulier, F. Learning from data: Concepts, theory, and methods. New York: Wiley.
5. Kennedy J, Eberhart R C, Particle swarm optimization, Proceedings of IEEE International Conference on Neutral Networks, Perth, Australia, 1995,pp. 1942-1948.
6. Shi Y H, Eberhart R C, A modified particle swarm optimizer, IEEE International Conference on Evolutionary computation, Anchorage, Alaska, 1998,pp. 69-73.
7. R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, in proc. 6th Symp. Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 34-44.
8. Konstantinos E. Parasopoulos and Michael N. Vrahatis, All global minimizers through particle swarm optimization", IEEE Trans, on Evolutionary Computation, vol. 8, No. 3, June 2004.
9. K.E. Parsopoulos and M.N. Vrahatis. Particle swarm optimization method for constrained optimization problems. In P. Sincak, J. Vascak, V. Kvasnicka, and J. Pospichal, editors, Intelligent TechnologiesTheory and Application: New Trends in Intelligent Technologies, volume 76 of Frontiers in Artificial Intelligence and Applications, pages 214-220. IOS Press, 2002.
10. J. F. Scutte, J. A. Reinbolt, B. J. Fregly, R. T. Haftka and A. D. George, Parallel global optimization with the particle swarm algorithm, Int. Journal for Numerical Methods in Engineering 2004; 61:2296-2315. (Pubitemid 40008433)
11. Raymond R. Tan, Hybrid evolutionary computation for the development of pollution prevention and control strategies, Journal of Cleaner Production 15 (2007) 902-906.
12. G. Box and G.M. Jenkins, Time Series Analysis: Forecasting and Control, San Francisco, Holden-Day, 1976.
13. Vandenberghe, V., Bauwens, W., Vanrolleghem, P.A., 2007. Evaluation of uncertainty propagation into river water quality predictions to guide future monitoring campaigns. Environmental Modelling & Software, 22(5):725-732.
14. Lee, J.H.W., Huang, Y., Dickman, M., Jayawardena, A.W., 2003. Neural network modeling of coastal algal blooms. Ecological Modelling, 159(2-3):179-201.
15. Kurunç, A., Yürekli, K., Çevik, O., 2005. Performance of two stochastic approaches for forecasting water quality and stream flow data from Ye§ihrmak River, Turkey. Environmental Modelling & Software, 20(9): 1195-1200