Volume 8 Number 11 (Nov. 2013)
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JCP 2013 Vol.8(11): 2908-2915 ISSN: 1796-203X
doi: 10.4304/jcp.8.11.2908-2915

Prediction of Mine Gas Emission Rate using Support Vector Regression and Chaotic Particle Swarm Optimization Algorithm

Qian Meng1, 2, Xiaoping Ma1, and Yan Zhou2
1 School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou , Jiangsu, China
2 School of Computer Science and technology, Jiangsu Normal University, Xuzhou, Jiangsu, China


Abstract—Forecasting of gas emission rate in mine is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector regression (SVR) can solve the problem with small samples, nonlinear and high dimensions. However, the precision of SVR is significantly affected by its parameter. In order to improve the mine gas emission rate accurately, an optimal selection approach of support vector regression parameters is proposed based on the chaotic particle swarm optimization algorithm (CPSO). A model based on the CPSO-SVR to predict the mine gas emission rate is established and the optimal parameters of SVR is searched by CPSO. The experimental data from a coal mine in China is used to illustrate the performance of proposed CPSO–SVR model. The results show that the proposed prediction model has better results than the artificial neural network (ANN) and traditional SVR algorithm under the circumstances of small sample. This indicates that the precision can meet the requirement of practical production and demonstrates that the CPSO is an effective approach for parameter optimization of SVR.

Index Terms—support vector regression, chaotic particle swarm optimization, mine gas emission rate

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Cite: Qian Meng, Xiaoping Ma, and Yan Zhou, " Prediction of Mine Gas Emission Rate using Support Vector Regression and Chaotic Particle Swarm Optimization Algorithm," Journal of Computers vol. 8, no. 11, pp. 2908-2915, 2013.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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