Gas outburst prediction model using rough set and support vector machine

Abstract

This paper is concerned with the problem of gas outburst prediction in coal mine working face. To predict the gas outburst accurately, this paper uses the rough set theory (RS) and support vector machine (SVM) to establish the prediction model. Firstly, based on the analysis of influencing factors of gas outburst, 10 factors including coal thickness variations, geological structures and gas change are selected as the influencing factors. By using the attribute reduction algorithm to eliminate redundant information, the gas outburst influencing factors as input to the prediction model are reduced from 10 to 6 in decision table. Secondly, by applying the particle swarm optimization (PSO) algorithm to optimize penalty parameter and kernel function of SVM and improve the generalization performance of model, the nonlinear relationship between main influencing factors and intensity of gas outburst is established. Finally, 60 sets of data of Jiulishan Coal Mine in Henan are used as training and testing samples to verify the proposed prediction model, and the discriminant results is compared with that of RBF model and SVM model. The results show that the prediction accuracy of the proposed model is 93%, which is improved compared with the other two models. The RS-PSOSVM model can reduce data redundancy, avoid the model to fall into the local extremum, and can predict the risk level of gas outburst effectively.

This is a preview of subscription content, access via your institution.

Fig.1
Fig.2
Fig.3
Fig.4

References

  1. 1.

    Liyi Z, Wensheng Lv, Peng Y et al (2018) Statistical analysis and occurrence laws of coal mine accidents of China from 2007 to 2016[J]. Saf Coal Mines 49(7):237–240

    Google Scholar 

  2. 2.

    Gao Si, Zhao Jiannan Hu, Qianting. (2018) Analysis of causes of coal and gas outburst accidents based on big data theory [J]. J Xi'an Univ Sci Technol 38(04):515–522

    Google Scholar 

  3. 3.

    Fang Qu, Long Z (2012) Development of coal and gas outburst prediction system based on BP neural network [J]. China Saf Sci J 22(1):11–15

    Google Scholar 

  4. 4.

    Sarivougioukas J, Vagelatos A (2020) Modeling deep learning neural networks with denotational mathematics in UbiHealth environment. Int J Softw Sci Comput Intell 12(3):14–20

    Article  Google Scholar 

  5. 5.

    Liangshan S (2009) Disaster prediction of coal mine gas based on rough set theory [J]. J China Coal Soc 34(3):371–375

    Google Scholar 

  6. 6.

    Haibo L, Fuzhong W, Yujie D (2018) Application of fuzzy data fusion in prediction of gas outburst in mining face[J]. Process Autom Instrum 39(5):89–92

    Google Scholar 

  7. 7.

    Hua FYX (2011) Prediction of coal and gas outburst based on data fusion and case-based reasoning[J]. J Southeast Uni (Nat Sci Ed) 41(S1):59–63

    MathSciNet  Google Scholar 

  8. 8.

    Guomin X, Minzhu S, Ming L (2016) Coal and Gas Outburst Intensity Prediction of FOA-SVM Model and Application[J]. Chin J Sens Actuators 29(12):1941–1946

    Google Scholar 

  9. 9.

    Zhong W, Zhuang Y, Sun J, Gu J (2019) Load forecasting for cloud computing based on wavelet support vector machine. Int J High Perform Comput Netw 14(3):315–324

    Article  Google Scholar 

  10. 10.

    Agrawal PK., Gupta BB, Jain S (2011) SVM based scheme for predicting number of zombies in a ddos attack. European intelligence and security informatics conference. IEEE, 178-182

  11. 11.

    Tingxin W, Hongjuan S, Bo Z et al (2019) Prediction model for outburst of coal and gas based on QGA-LSSVM. J Saf Sci Technol 11(5):5–12

    Google Scholar 

  12. 12.

    Jinsong Wu, Song G, Jie Li, Deze Z (2016) Big data meet green challenges: big data toward green applications. IEEE Syst J 10(3):888–900

    Article  Google Scholar 

  13. 13.

    Jinsong Wu, Song G, Huawei H, William L, Yong X (2018) Information and communications technologies for sustainable development goals: state-of-the-art, needs and perspectives. IEEE Commun Surv Tutor 20(3):2389–2406

    Article  Google Scholar 

  14. 14.

    Jinsong Wu, Song G, Jie Li, Deze Z (2016) Big data meet green challenges: greening big data. IEEE Syst J 10(3):873–887

    Article  Google Scholar 

  15. 15.

    Pawlak Z (1982) Rough sets[J]. Int J Inf Compt Sci 11:241–256

    Google Scholar 

  16. 16.

    Brezinski K, Guevarra M, Ferens K (2020) Population Based Equilibrium in Hybrid SA/PSO for Combinatorial Optimization: Hybrid SA/PSO for Combinatorial Optimization. Int J Softw Sci Comput Intell 12(2):13–21

    Article  Google Scholar 

  17. 17.

    Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466

    Article  Google Scholar 

  18. 18.

    Liyuan Z, Yishan P (2018) Mechanisms of rockburst and outburst compound disaster in deep mine[J]. J China Coal Soc 43(11):3042–3050

    Google Scholar 

  19. 19.

    Hadj Ahmed Bouarara (2019) A Computer-assisted diagnostic (CAD) of screening mammography to detect breast cancer without a surgical biopsy. Int J Softw Sci Comput Intell 11(4):19–27

    Google Scholar 

  20. 20.

    Patil DR, Patil JB (2019) Malicious web pages detection using feature selection techniques and machine learning. Int J High Perform Comput Netw 14(4):473–488

    Article  Google Scholar 

  21. 21.

    Ning L, Liguan W, Mingtao J (2017) Rockburst prediction based on rough set theory and support vector machine[J]. J Cent South Univ (Sci Technol) 48(5):1268–1275

    Google Scholar 

  22. 22.

    Ren F, Shi AQ, Yang ZJ (2018) Research on load identification of mine hoist based on improved support vector machine[J]. Trans Can Soc Mech Eng 42(3):201–210

    Article  Google Scholar 

  23. 23.

    Yingjie Li, Yongguo Y (2017) Prediction of coal and gas outburst based on improved PSO optimizing parameters of LSSVM. Coal Technol 36(9):129–131

    Google Scholar 

  24. 24.

    Hua Fu, Shengcheng F, Zhenbiao G, Yugang Y (2018) Study on double coupling algorithm based model for coal and gas outburst prediction. China Saf Sci J 28(3):84–89

    Google Scholar 

  25. 25.

    Vapnik VN (1998) Statistical learning theory [M]. John Wiley, New York

    Google Scholar 

  26. 26.

    Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering. Springer, Berlin

    Google Scholar 

  27. 27.

    Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047–4071

    Article  Google Scholar 

  28. 28.

    Al-Smadi M, Qawasmeh O, Al-Ayyoub M, Jararweh Y, Gupta B (2018) Deep recurrent neural network vs support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. J Comput Sci 27:386–393

    Article  Google Scholar 

  29. 29.

    Shrestha AP, Yoo SJ (2018) Optimal resource allocation using support vector machine for wireless power transfer in cognitive radio networks[J]. IEEE Trans On Veh Technol 67(9):8525–8535

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by Key Project of Science and Technology of Education Department of Henan Province (19B120002) and Key Laboratory of Control Engineering of Henan Province (KG2016-17).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Liu Haibo.

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

Verify currency and authenticity via CrossMark

Cite this article

Haibo, L., Yujie, D. & Fuzhong, W. Gas outburst prediction model using rough set and support vector machine. Evol. Intel. (2020). https://doi.org/10.1007/s12065-020-00507-4

Download citation

Keywords

  • Gas outburst
  • Rough set theory
  • Support vector machine
  • Particle swarm optimization
  • Prediction