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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 858260, 8 pages
http://dx.doi.org/10.1155/2014/858260
Research Article

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

1School of Information and Electrical Engineering, China University Mining & Technology, Xuzhou, Jiangsu 221116, China
2School of Medical Informatics, Xuzhou Medical College, Science and Technology Building E206, Dong Dian Zi Campus, Xuzhou, Jiangsu 220009, China
3Northern Nenghua Company of Wanbei Coal-Electricity Group, Huaibei, Anhui 235000, China
4School of Medical Imaging, Xuzhou Medical College, Xuzhou, Jiangsu 221009, China

Received 18 February 2014; Accepted 5 July 2014; Published 22 July 2014

Academic Editor: Marcelo J. Colaço

Copyright © 2014 Wu Xiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. N. Zhou and B. Q. Lin, Coalbed Gas Occurrence and Flow Theory, China Coal Industry Publishing House, Beijing, China, 1999.
  2. J. Q. Zhang, “The application of analytic hierarchy process in mine gas prevention system,” Procedia Engineering, vol. 26, pp. 1576–1584, 2011. View at Publisher · View at Google Scholar
  3. W. Yang, B. Lin, C. Zhai, X. Li, and S. An, “How in situ stresses and the driving cycle footage affect the gas outburst risk of driving coal mine roadway,” Tunnelling and Underground Space Technology, vol. 31, pp. 139–148, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. K. X. Wang, X. H. Fu, Y. A. Zhou, Y. HE, and H. Wu, “Dynamic development characteristics of amounts of gas and levels of pressure in the Pan-1 coal mine of Huainan,” Mining Science and Technology, vol. 19, no. 6, pp. 740–744, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Wang and J. Yan, “The geological factor analysis of influenced Tianchi coal mine gas occurrence,” Procedia Engineering, vol. 45, pp. 317–321, 2012. View at Google Scholar
  6. L. Wang, Y. Cheng, L. Wang, P. Guo, and W. Li, “Safety line method for the prediction of deep coal-seam gas pressure and its application in coal mines,” Safety Science, vol. 50, no. 3, pp. 523–529, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Cheng, J. Y. Bai, and J. S. Qian, “Short-term forecasting method of coal mine gas concentration based on chaotic time series,” Journal of China University of Mining and Technology, vol. 2, no. 37, pp. 231–235, 2008. View at Google Scholar · View at Scopus
  8. X. Y. Cui and G. Q. Zhao, “Application of chaos forecast of mine gas density monitoring,” Journal of Liaoning Technical University, vol. 27, no. 1, pp. 184–186, 2008. View at Google Scholar
  9. X. J. Ni, “Forecasting of gas emissions based on chaotic time series,” Science & Technology Information, vol. 31, no. 1, pp. 20–34, 2008. View at Google Scholar
  10. J. N. Pan, Z. P. Meng, and Y. C. Liu, “Grey smoothing model for predicting mine gas emission,” Journal of China University of Mining & Technology, vol. 13, no. 1, pp. 76–78, 2003. View at Google Scholar
  11. P. Lu, Y. Ma, and X. Zhou, “Research and application on dynamic forecasting model of gas consistence in top corner,” Journal of the China Coal Society, vol. 31, no. 4, pp. 461–465, 2006. View at Google Scholar · View at Scopus
  12. J. Y. Zhang, J. Cheng, and Y. H. Hou, “Forecasting coalmine gas concentration based on adaptive neuro-fuzzy inference system,” Journal of China University of Mining and Technology, vol. 36, no. 4, pp. 494–498, 2007. View at Google Scholar · View at Scopus
  13. X. Sun, “Coalmine gas prediction based on fuzzy neural network,” Journal of Anhui University of Technology, vol. 29, no. 3, pp. 229–232, 2012. View at Google Scholar
  14. J. H. Zhao and Q. Xu, “Short-term prediction of coalmine gas concentration based on chaotic series and wavelet neural network,” in Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence (AICI '10), vol. 3, pp. 240–244, IEEE, Sanya, China, October 2010. View at Publisher · View at Google Scholar
  15. L. Gao, Y. Hu, and H. Yu, “Prediction of gas emission time series based on W-RBF,” Journal of the China Coal Society, vol. 33, no. 1, pp. 67–70, 2008. View at Google Scholar · View at Scopus
  16. X. Wang, J. Liu, and J. Lu, “Gas concentration forecasting approach based on wavelet transform and optimized predictor,” Journal of Basic Science and Engineering, vol. 19, no. 3, pp. 499–508, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. W. T. Ma, “Gas emission forecast based on wavelet transform and genetic-least square support vector machine,” Journal of Mining and Safety Engineering, vol. 26, no. 4, pp. 524–528, 2009. View at Google Scholar · View at Scopus
  18. Q. Wang and J. Cheng, “Forecast of coalmine gas concentration based on the immune neural network model,” Journal of the China Coal Society, vol. 33, no. 6, pp. 665–669, 2008. View at Google Scholar · View at Scopus
  19. M. Y. Qiao, X. P. Ma, and J. Y. Lan, “Time series short-Term gas prediction based on weighted LS-SVM,” Journal of Mining and Safety Engineering, vol. 28, no. 2, pp. 310–314, 2011. View at Google Scholar · View at Scopus
  20. D. W. Dong, S. G. Li, X. T. Chang, and H. F. Lin, “Prediction model of gas concentration around working face using multivariate time series,” Journal of Mining and Safety Engineering, vol. 29, no. 1, pp. 135–139, 2012. View at Google Scholar · View at Scopus
  21. L. Gao and H. Yu, “Prediction of gas emission based on information fusion and chaotic time series,” Journal of China University of Mining and Technology, vol. 16, no. 1, pp. 94–96, 2006. View at Google Scholar · View at Scopus
  22. L. Shao and G. Fu, “Dynamic prediction technology for gas based on data fusion theory,” Journal of the China Coal Society, vol. 33, no. 5, pp. 551–555, 2008. View at Google Scholar · View at Scopus
  23. D. Li, Y. Cheng, L. Wang, H. Wang, L. Wang, and H. Zhou, “Prediction method for risks of coal and gas outbursts based on spatial chaos theory using gas desorption index of drill cuttings,” Mining Science and Technology, vol. 21, no. 3, pp. 439–443, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. Q. Meng, X. P. Ma, and Y. Zhou, “Prediction of mine gas emission rate using support vector regression and chaotic particle,” Journal of Computers, vol. 8, no. 11, pp. 2908–2915, 2013. View at Google Scholar
  25. F. Z. Wang and W. Z. Liu, “Prediction strategy of coal and gas outburst based on artificial neural network,” Journal of Computers, vol. 8, no. 1, pp. 240–247, 2013. View at Publisher · View at Google Scholar · View at Scopus
  26. W. Ting, C. Lin-Qin, F. Yao, and Z. Tingcheng, “A wavelet-based robust relevance vector machine based on sensor data scheduling control for modeling mine gas gushing forecasting on virtual environment,” Mathematical Problems in Engineering, vol. 2013, Article ID 579693, 4 pages, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. N. A. Shrivastava and B. K. Panigrahi, “A hybrid wavelet-ELM based short term price forecasting for electricity markets,” Electrical Power and Energy Systems, vol. 55, no. 7, pp. 41–50, 2014. View at Google Scholar
  28. Y. Q. Liu, J. Shi, Y. P. Yang, and W. J. Lee, “Short-term wind-power prediction based on wavelet transform-support vector machine and statistic-characteristics analysis,” IEEE Transactions on Industry Applications, vol. 48, no. 4, pp. 1136–1141, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. G. Capizzi, C. Napoli, and F. Bonanno, “Innovative second-generation wavelets construction with recurrent neural networks for solar radiation forecasting,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 11, pp. 1805–1815, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. A. U. Haque, P. Mandal, J. L. Meng, A. K. Srivastava, T. L. Tseng, and T. Senjyu, “A novel hybrid approach based on wavelet transform and fuzzy ARTMAP networks for predicting wind farm power production,” IEEE Transactions on Industry Applications, vol. 49, no. 5, pp. 2253–2261, 2013. View at Google Scholar
  31. G. G. He, S. F. Ma, and Y. Li, “A study on forecasting for time series based on wavelet analysis,” Acta Automatica Sinica, vol. 29, no. 1, pp. 1012–1014, 2012. View at Google Scholar
  32. A. S. Pandey, D. Singh, and S. K. Sinha, “Intelligent hybrid wavelet models for short-term load forecasting,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1266–1273, 2010. View at Publisher · View at Google Scholar · View at Scopus
  33. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Scopus
  34. M. Yang, S. Fan, and W. J. Lee, “Probabilistic short-term wind power forecast using componential sparse Bayesian learning,” IEEE Transactions on Industry Applications, vol. 49, no. 6, pp. 2783–2791, 2013. View at Google Scholar
  35. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  36. D. E. Rumeihart, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, MIT Press, Cambridge, Mass, USA, 1986.
  37. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  38. Q. Wang and S. Chen, “Ensemble learning of ELM regressors based on l1-regularization,” Journal of Computer Research and Development, vol. 49, no. 12, pp. 2631–2637, 2012. View at Google Scholar
  39. C. R. Rao and S. K. Mitra, Generalized Inverse of Matrices and Its Applications, John Wiley & Sons, New York, NY, USA, 1971. View at MathSciNet
  40. D. Serre, Matrices: Theory and Applications, vol. 216 of Graduate Texts in Mathematics, Springer, New York, NY, USA, 2002. View at MathSciNet
  41. L. Yang and R. Zhang, “Online sequential ELM algorithm and its improvement,” Journal of Northwest University (Natural Science Edition), vol. 42, no. 6, pp. 885–896, 2012. View at Google Scholar · View at MathSciNet
  42. P. L. Bartlett, “The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network,” IEEE Transactions on Information Theory, vol. 44, no. 2, pp. 525–536, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  43. A. Cohen, I. Daubechies, and J. Feauveau, “Biorthogonal bases of compactly supported wavelets,” Communications on Pure and Applied Mathematics, vol. 45, no. 5, pp. 485–560, 1992. View at Publisher · View at Google Scholar · View at MathSciNet
  44. H. S. Kim, R. Eykholt, and J. D. Salas, “Nonlinear dynamics, delay times, and embedding windows,” Physica D, vol. 127, no. 1-2, pp. 48–60, 1999. View at Publisher · View at Google Scholar · View at Scopus
  45. L. Breiman, J. Friedman, C. Stone, and R. A. Olshen, Classification and Regression Trees, Chapman & Hall, 1984.
  46. J. Kennedy and R. Eberhart, “Particle swarm optimization,” in Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Perth, Australia, November-December 1995. View at Publisher · View at Google Scholar · View at Scopus