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An Attention-Based Spatio-temporal Model for Methane Concentration Forecasting in Coal Mine

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Abstract

Methane is a kind of harmful gas produced in the process of coal mining. Because of its flammable and explosive characteristics, it poses a great threat to safety during coal mining. In practice, accurate and real-time gas concentration forecasting is becoming an essential issue for reducing methane risks and accidents. Given the complex spatial correlation and temporal variation of sensor data in a coal mine monitoring system, deep learning algorithms have been widely applied due to their revolutionary feature representation capability. However, existing deep learning models utilize recurrent neural networks, which can barely provide satisfactory accuracy due to their ignorance of realistic working conditions of a coal face or an insufficiency in capturing representative spatio-temporal patterns. In this paper, we propose an attention-based spatio-temporal encoder–decoder network approach, named the ASTED model, for methane concentration forecasting. The ASTED model is built based on the integration of the spatial, temporal and environmental information. Specifically, the multi-attention mechanism is used to learn the dynamic spatio-temporal dependencies, and the feature fusion module is used to incorporate the data from different mine sensors. Finally, we employ the LSTM-based encoder–decoder model to generate the final prediction results. Experiments demonstrate that the ASTED model can obtain the dynamic spatio-temporal correlation from multiple sensor readings and achieve the best performance compared with various state-of-the-art solutions.

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References

  1. Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473

  2. Cao J, Li WC (2017) Numerical simulation of gas migration into mining-induced fracture network in the goaf. Int J Min Sci Technol 27(4):681–685

    Article  Google Scholar 

  3. Cheng J, Bai JY, Qian JS, et al (2008) Short-term forecasting method of coalmine gas concentration based on chaotic time series. Zhongguo Kuangye Daxue Xuebao 37

  4. Cho K, Merrienboer BV, Gulcehre C, et al (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

  5. Dong DW, Li SG, Chang XT et al (2012) Prediction model of gas concentration around working face using multivariate time series. J Min Saf Eng 29(1):135–139

    Google Scholar 

  6. Fu H, Liu Y, Li H et al (2015) Short term forecasting model of gas concentration in coal mine using the capso-enn. Chin J Sens Actuators 28(5):717–722

    Google Scholar 

  7. Gao L, Hu YJ, Yu HZ (2008) Prediction of gas emission time series based on w-rbf. Mei T’an Hsueh Pao (Journal of China Coal Society) 33:60–70

    Google Scholar 

  8. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  9. Jensen B, Gillies S, Jones N et al (1992) Review of methane emission and prediction research in longwall coal mines. Aust Inst Min Metall Proc 1:11–17

    Google Scholar 

  10. Karacan CÖ, Ruiz FA, Cotè M et al (2011) Coal mine methane: a review of capture and utilization practices with benefits to mining safety and to greenhouse gas reduction. Int J Coal Geol 86(2–3):121–156

    Article  Google Scholar 

  11. Kurnia JC, Sasmito AP, Mujumdar AS (2014) Cfd simulation of methane dispersion and innovative methane management in underground mining faces. Appl Math Model 38(14):3467–3484

    Article  MathSciNet  Google Scholar 

  12. Liang Y, Ke S, Zhang J, et al (2018) Geoman: multi-level attention networks for geo-sensory time series prediction. In: IJCAI, pp 3428–3434

  13. Lu G, Li X, Zu B et al (2017) Research on time-varying series forecasting of gas emission quantity based on emd-mfoa-elm. J Saf Sci Technol 13(6):109–114

    Google Scholar 

  14. Lu P, Ma YG, Zhou XQ (2006) Research and application on dynamic forecasting model of gas consistence in top corner. J China Coal Soc 031(004):461–465

    Google Scholar 

  15. Lyu P, Chen N, Mao S et al (2020) Lstm based encoder-decoder for short-term predictions of gas concentration using multi-sensor fusion. Process Saf Environ Prot 137:93–105

    Article  Google Scholar 

  16. Mnih V, Heess N, Graves A, et al (2014) Recurrent models of visual attention. In: Advances in neural information processing systems, pp 2204–2212

  17. Ni X (2008) Forecasting of gas emissions based on chaotic time series. Sci Technol Inf 31:34+20

    Google Scholar 

  18. Noack K (1998) Control of gas emissions in underground coal mines. Int J Coal Geol 35(1–4):57–82

    Article  Google Scholar 

  19. Pan J, Meng ZP, Liu YC (2003) Grey smoothing model for predicting mine gas emission. J China Univ Min Technol 13(1):76–78

    Google Scholar 

  20. Qiao MY, Ma XP, Lan JY et al (2011) Time series short-term gas prediction based on weighted ls-svm. Caikuang yu Anquan Gongcheng Xuebao/Journal of Mining and Safety Engineering 28(2):310–314

    Google Scholar 

  21. Ślȩzak D, Grzegorowski M, Janusz A et al (2018) A framework for learning and embedding multi-sensor forecasting models into a decision support system: a case study of methane concentration in coal mines. Inf Sci 451–452:112–133

    Article  MathSciNet  Google Scholar 

  22. Song Y, Yang S, Hu X et al (2019) Prediction of gas and coal spontaneous combustion coexisting disaster through the chaotic characteristic analysis of gas indexes in goaf gas extraction. Process Saf Environ Prot 129:8–16

    Article  Google Scholar 

  23. Sun X (2012) Coalmine gas prediction based on fuzzy neural network. J Anhui Univ Technol 29:229

    Google Scholar 

  24. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112

  25. Tutak M, Brodny J (2019) Predicting methane concentration in longwall regions using artificial neural networks. Int J Environ Res Public Health 16(8):1406

    Article  Google Scholar 

  26. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  27. Wang K, Fu X, Zhou Y (2009) Dynamic development characteristics of amounts of gas and levels of pressure in the pan-1 coal mine of huainan. Min Sci Technol 19(6):740–744

    Google Scholar 

  28. Wang L, Cheng YP, Wang L et al (2012) Safety line method for the prediction of deep coal-seam gas pressure and its application in coal mines. Saf Sci 50(3):523–529

    Article  Google Scholar 

  29. Wei Y, Lin BQ, Zhai C et al (2012) How in situ stresses and the driving cycle footage affect the gas outburst risk of driving coal mine roadway -sciencedirect. Tunn Undergr Space Technol 31:139–148

    Article  Google Scholar 

  30. Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal arima process: theoretical basis and empirical results. J Transp Eng 129(6):664–672

    Article  Google Scholar 

  31. Wu J, Yuan S, Zhang C et al (2018) Numerical estimation of gas release and dispersion in coal mine using ensemble kalman filter. J Loss Prev Process Ind 56:57–67

    Article  Google Scholar 

  32. Wu X, Qian J, Huang C et al (2014) Short-term coalmine gas concentration prediction based on wavelet transform and extreme learning machine. Math Probl Eng 2014:1–8

    Google Scholar 

  33. Xia T, Zhou F, Wang X et al (2016) Controlling factors of symbiotic disaster between coal gas and spontaneous combustion in longwall mining gobs. Fuel 182:886–896

    Article  Google Scholar 

  34. Xia T, Zhou F, Wang X et al (2017) Safety evaluation of combustion-prone longwall mining gobs induced by gas extraction: a simulation study. Process Saf Environ Prot 109:677–687

    Article  Google Scholar 

  35. Xu K, Ba J, Kiros R, et al (2015) Show, attend and tell: Neural image caption generation with visual attention. In: International conference on machine learning, PMLR, pp 2048–2057

  36. Xu Q, Zhao J, Wang X (2010) Short-term prediction of coalmine gas concentration based on chaotic series and wavelet neural network. In: 2010 International conference on artificial intelligence and computational intelligence, IEEE, pp 240–244

  37. Yu Q, Kai W, Yang S (2000) Study on pattern and control of gas emission at coal face in china. J China Univ Min Technol 29(1):9–14

    Google Scholar 

  38. Zhang JY, Cheng J, Hou YH et al (2007) Forecasting coalmine gas concentration based on adaptive neuro-fuzzy inference system. J China Univ Min Technol 36(4):494–498

    Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (72271034) and BUPT Excellent Ph.D. Students Foundation (CX2021132).

Funding

National Natural Science Foundation of China (72271034); BUPT Excellent Ph.D. Students Foundation (CX2021132).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yifei Gao & Xiaohang Zhang

  2. Key Laboratory of Trustworthy Distributed Computing and Service, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yifei Gao & Xiaohang Zhang

  3. School of Modern Post, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Zhengren Li

  4. Business School, Beijing Language and Culture University, Beijing, 100083, China

    Yu Du

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  1. Yifei Gao
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  2. Xiaohang Zhang
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  4. Yu Du
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Correspondence to Xiaohang Zhang.

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Gao, Y., Zhang, X., Li, Z. et al. An Attention-Based Spatio-temporal Model for Methane Concentration Forecasting in Coal Mine. Neural Process Lett 55, 4777–4798 (2023). https://doi.org/10.1007/s11063-022-11065-4

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