Skip to main content
SpringerLink
Log in

Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+FICA–LDA and optimized QPSO-DELM classifier

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary; the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, discrete wavelet transform (DWT) is utilized as a preprocessing technique to decompose subseries and extract the features with different frequencies and the optimal feature components are retained; second, in order to eliminate the redundancy between the features and uncorrelation between features and outbursts, we use the fast independent component analysis (FICA) to obtain each independent component, obtaining the global information in the feature; then, the obtained features are input into linear discriminant analysis (LDA), under the guidance of class labels, then the local information in features is obtained; finally, the projected features are input into the deep extreme learning machine (DELM) classifier based on the optimal parameters by quantum particle swarm optimization (QPSO) for training and classification. The experimental results on the dataset of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Availability of data and material

The raw/processed data required to reproduce these findings cannot be shared at this time as the data also form part of an ongoing study. Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

References

  1. Chen KP (2011) A new mechanistic model for prediction of instantaneous coal outbursts Dedicated to the memory of Prof. Daniel D. Joseph. Inter J Coal Geol 87(2):72–79.

  2. Cunquan N, Xiating F (2005) Coal and gas outburst area prediction using SVM. Chin J Rock Mechan Eng 24(2):263–267

    Google Scholar 

  3. Wang W, Ding J (2003) Wavelet network model and its application to the prediction of hydrology. Nat Sci 1:67–71

    Google Scholar 

  4. Tan Z, Zhang J, Wang J, Xu J (2010) Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 87(11):3606–3610

    Article  Google Scholar 

  5. Daubechiesi. (1992) Ten lectures on wavelets, 61. SIAM, Philadelphia

    Book  Google Scholar 

  6. Zhu Zj, Zhang HW, Han J, Song WH (2013) Prediction of coal and gas outburst based on PCA-BP neural network. China Safety Sci J 23:45–51

    Google Scholar 

  7. Fu H, Wang X, Wang Z (2014) Research on the soft sensor of coal and gas outburst based on PCA and PSO-ELM. Chinese J Sens Actuators 27(12):1710–1715

    Google Scholar 

  8. Li D (2009) Research on prediction of coal and gas outburst based on ICA-SVM. Industry and Mine Automation 10:36–39

    Google Scholar 

  9. Gao W (2010) The application of fisher discriminant in predicting dangerous level of coal and gas outburst. China Safety Sci J 20(10):26–31

    Google Scholar 

  10. Wen T, Zhang B, Shao L (2014) Prediction of coal and gas outburst based on random forest model. Comput Eng Appl 50(10):233–238

    Google Scholar 

  11. Long NZ, Yi AM, A XJ, Zhang XL (2019) Prediction of coal and gas outburst intensity based on LLE-FOA-BP model. Ind Mine Autom 45(10):68–74

    Google Scholar 

  12. Wang Y, Wang, Fu H, Zhang Y (2015) The identification model of coal and gas outburst intensity based on KPCA and CIPSO-PNN. Chinese J Sens Actuators 28(2):272–279

    Google Scholar 

  13. Li S, Hu H (2017) Risk identification of coal and gas outburst based on KPCA and improved extreme learning machine model. Appl Res Computers 35(1):172–177

    MathSciNet  Google Scholar 

  14. Kang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178–184

    Article  Google Scholar 

  15. Fu H, Feng S, Gao Z, Yang Y (2018) Study on double coupling algorithm Based model for coal and gas outburst prediction. China Safety Sci J 28(3):84–90

    Google Scholar 

  16. Liang B, Qin B, Sun W, Wang S, Shi Y (2013) The application of intelligent weighting grey target decision model in the assessment of coal-gas outburst. J China Coal Soc 38(7):1611–1615

    Google Scholar 

  17. Yan X, Fu H, Chen W (2014) Prediction of coal mine gas emission based on Markov chain improving IGA-BP mode. Computer Model New Technol 10:2–24

    Google Scholar 

  18. Kuang F, Jin Z, Xu W (2014) A novel chaotic artificial bee colony algorithm based on Tent map. In: 2014 IEEE congress on evolutionary computation (CEC) 235–241.

  19. Hoang ND, Bui DT (2016) A novel relevance vector machine classifier with cuckoo search optimization for spatial prediction of landslides. J Comput Civ Eng 30(5):1943–5487

    Article  Google Scholar 

  20. Ahilaa R, Sadasivamb V, Manimalaa K (2015) An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances. Appl Soft Comput 32:23–37

    Article  Google Scholar 

  21. Liying P, Fan G (2016) Hybridizing DEMD and quantum PSO with SVR in electric load forecasting. Energ 22(1):1–20

    Google Scholar 

  22. Santoso S, Grady WM, Powers EJ, Lamoure J, Bhatt SC (2000) Characterization of distribution power quality events with Fourier and Wavelet transforms. IEEE Trans Power Deliv 15(1):245–247

    Google Scholar 

  23. Gaouda M, Salama MMA, Sultan MR, Chikhani AY (1999) Power quality detection and classification using wavelet multiresolution signal decomposition. IEEE Trans Power Deliv 14(4):1469–1476

    Article  Google Scholar 

  24. Mário O, Coury Denis V, Delmont FO, Usida Wesley F, Adriano AFM, Carneiro, et al (2009) Power quality analysis applying a hybrid methodology with wavelet transforms and neural networks. Int J Electr Power Energy Syst 31(5):206–212

    Article  Google Scholar 

  25. Subasi A, Gursoy MI (2010) EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst Appl 37(12):8659–8666

    Article  Google Scholar 

  26. Kao L-J et al (2013) Integration of nonlinear independent component analysis and support vector regression for stock price forecasting. Neurocomputing 99:534–542

    Article  Google Scholar 

  27. HYVARINEN (1999) A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3): 626–634.

  28. Wei Z, Yuanliang H, Zhenzhen H et al (2017) Research on implementation method of LDA algorithm based on Particle Swarm Optimization. Comput Eng Appl 53(1):39–43

    Google Scholar 

  29. Liu L, Yang H, Xiao G (2019) Time-frequency domain feature extraction algorithm based on linear discriminant analysis. Syst Eng Electronic 2(7):14–19

    Google Scholar 

  30. Sijing D, Zhong S, Sun Y, Yanku. (2019) Combining principal component analysis network with linear discriminant analysis for the classification of retinal optical coherence tomography images. J Image Graphic 24(01):0115–0123

    Google Scholar 

  31. Huang GB, Zhou HM, Ding XJ, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42(2):345–367

    Google Scholar 

  32. Xu SuHui Mu, XiaoDong CD, Chang L (2018) Domain adaption algorithm with ELM parameter transfer. Acta automatic sinica 44(2):311–317

    Google Scholar 

  33. Ding SF, Xu XZ, NIER. (2014) Extreme Learning Machine and Its Applications. Neural Comput Appl 25(34):549–556

    Article  Google Scholar 

  34. JiaMing Xu, WeiQiang Z (2015) Manifold regularized extreme learning machine for language recognition. Acta automatic sinica 41(9):234–250

    Google Scholar 

  35. Shan Dong Xu, Xinzheng. (2017) Multi-label learning model based on multi-label radial basis function neural network and regularized extreme learning machine. PR&AI 30(9):833–840

    Google Scholar 

  36. Uzair M, Shafait F, Ghanem B et al (2015) Representation learning with deep extreme learning machines for efficient image set classification. MACINTYRE. J Neural Computing Appl London: Springer 1:1–13

    Google Scholar 

  37. Sudheer Ch, Nitin Anand BK, Panigrahi SM (2013) Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing 101:18–23

    Article  Google Scholar 

  38. Zhang Y, Cui J, Jiao X (2018) Study of the multi-index coupling forecasting model of coal and gas outburst and its application. Chinese J Eng 40(11):1309–1316

    Google Scholar 

  39. Alpar O (2018) Online signature verification by continuous wavelet transformation of speed signals. Expert Syst Appl 104(3):33–42

    Article  Google Scholar 

  40. Lili Huang, Jun Wang (2018) Forecasting energy fluctuation model by wavelet decomposition and stochastic recurrent wavelet neural network. Neurocomputing.309:70–82.

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (U1704242); the authors of this paper would like to thank the insightful and valuable comments from Hebei Key Laboratory of IOT blockchain integration.

Author information

Authors and Affiliations

  1. School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing, P. R. China

    Xuning Liu, Zixian Zhang & Guoying Zhang

  2. Department of Computer Engineering, Shijiazhuang University, Shijiazhuang, P. R. China

    Xuning Liu & Zhixiang Li

  3. School of Foreign University, Liaocheng University, Liaocheng, P. R. China

    Zixian Zhang

Authors
  1. Xuning Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhixiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zixian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guoying Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zixian Zhang.

Ethics declarations

Conflict of interest

The authors declare no competing financial interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, X., Li, Z., Zhang, Z. et al. Coal and gas outbursts prediction based on combination of hybrid feature extraction DWT+FICA–LDA and optimized QPSO-DELM classifier. J Supercomput 78, 2909–2936 (2022). https://doi.org/10.1007/s11227-021-03964-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03964-5

Keywords

Search

Navigation