A novel prediction model for the degree of rescue safety in mine thermal dynamic disasters based on fuzzy analytical hierarchy process and extreme learning machine

A novel prediction model for the degree of rescue safety in mine thermal dynamic disasters based on fuzzy analytical hierarchy process and extreme learning machine

Jun GuoYin Liu Hao Yan Yaqi Xu 

School of Safety Science and Engineering, Xi’an University of Science and Technology, Xi'an 710054, China

Key Laboratory for Prevention and Control of Coal Fires in Shaanxi Province, Xi’an 710054, China

Key Laboratory of Western Mine and Hazard Prevention, Ministry of Education of China, Xi’an, Shaanxi, China

Corresponding Author Email: 
15 February 2018
| |
7 June 2018
| | Citation



Considering the rapid development, uncertain situations and prediction difficulty of mine thermal dynamic disasters (MTDDs), this paper combines the fuzzy analytical hierarchy process (FAHP) and extreme learning machine (ELM) into a prediction model to quantify the degree of MTDD rescue safety in a fast and accurate manner. Firstly, a static FAHP model was constructed by the Delphi, AHP, and fuzzy comprehensive evaluation (FCE) to assess various MTDD rescue cases, quantify the exact degree of rescue safety and provide sample data for real-time prediction based on artificial intelligence. Then, the improved ELM algorithm was introduced to design a dynamic prediction model for the safety of various MTDD rescue cases. Through case study, it is confirmed that the prior knowledge of experts was fully utilized by the model, outputting accurate, rational and reliable sample data. Moreover, the ELM algorithm enabled the intelligent extraction and rapid inference of sample data features. The prediction results are accurate, stable, and highly reliable, revealing that our model is a desirable tool for the real-time prediction of safety in mine rescue operations. The research findings provide new insights into the prediction of mine rescue safety.


mine thermal dynamic disaster (MTDD), fuzzy analytical hierarchy process (FAHP), extreme learning machine (ELM), degree of rescue safety

1. Introduction
2. Theoretical Framework
3. FAHP-Based Sample Data Acquisition
4. Simulation of Elm-Based Dynamic Prediction
5. Conclusion and Discussions

[1] Wang HY, Wang BJ. (2010). Mine thermal dynamic disaster science. Coal Industry Press, Beijing.

[2] Wen H. (2014). Emergency rescue in coal mine accident. China University of Mining and Technology Press, Xuzhou.

[3] Li B, Deng J, Xiao Y, Zhai XW, Shu CM, Gao W. (2018). Heat transfer capacity of heat pipes: An application in coalfield wildfire in China. Heat and Mass Transfer 54(6): 1755-1766. http://doi.org/10.1007/s00231-017-2262-6

[4] Deng J, Li B, Xiao Y, Ma L, Wang CP. (2017). Combustion properties of coal gangue using thermogravimetry–Fourier transform infrared spectroscopy. Applied Thermal Engineering 116: 244-252. http://doi.org/10.1016/j.applthermaleng.2017.01.083

[5] Wen H, Guo J, Jin YF, Zhang Z, Wang T, Liu WY. (2016). The present situation and trend of research on safety evaluation of disaster emergency rescue for China coal mine. Safety in Coal Mines 47(3): 172-178. http://doi.org/10.13347/j.cnki.mkaq.2016.03.047

[6] Zhao ZF, Wen H, Guo J. (2015). Comprehensive forecast of coal and gas outburst based on multiple methods. Safety in Coal Mines 46(11): 160-163.

[7] Xia XG, Huang QX. (2007). Application of AHP in determining weight of factors of capability of top coal caving. Journal of China Coal Society 32(10): 51-54. http://doi.org/10.1007/s10800-006-9244-6

[8] Cheng WM, Zhou G, Wang G. (2010). Evaluation method of miners’ safety behavior based on gray-fuzzy-improving momentum BP algorithm. Journal of China Coal Society 35(01): 101-105.

[9] Kors JA, Bemmel JH. (1989). The Delphi method: a review of its application in medicine. The Netherlands press, Netherlands.

[10] Saaty TL. (1980). The Analytic Hierarchy Process. Mcgraw-Hill, New York.

[11] Tao JC, Wu JM. (2001). New study on determining the weight of index in synthetic weighted mark method. Systems Engineering-theory & Practice 21(8): 78-96.

[12] Su BX, Zhang JL, Che XM. (2013). Performance evaluation of pulverized coal injection of blast furnace based on principle component analysis. Journal of China Coal Society 38(12): 2234-2240.

[13] Shi SL, Li RQ. (2010). Research and application of AHP-GT model of gas explosion accident evolution risk assessment in coal mine. Journal of China Coal Society 35(07): 1137-1141. http://doi.org/10.1016/S1876-3804(11)60004-9

[14] Hwang CL, Yoon KS. (1981). Multiple Attribute decision making. Springer, Berlin, Heidelberg. http://doi.org/10.1007/978-3-642-48318-9

[15] Deng JL. (1982). Control problems of grey system. System and Control Letter 1(5): 288-194. http://doi.org/10.1016/S0167-6911(82)80025-X

[16] Zhao ZF, Wen H, Gao WX, Guo J. (2016). Data mining and knowledge decision in the integrity management of long-distance pipeline. Journal of Xi'an Shiyou University (Natural Science Edition) 31(4): 109-114. http://doi.org/10.3969/j.issn.1673-064X.2016.04.019

[17] Sun Q, Ouyang J. (2015). Hesitant fuzzy multi-attribute decision making based on topsis with entropy-weighted method. Management Science & Engineering 9(3): 1-6. http://doi.org/10.1016/j.knosys.2013.05.011

[18] Guo J. (2016). Safety evaluation and dynamic prediction for the rescue operation after mine thermo dynamic disasters. Xi’an University of Science and Technology, Xi’an.

[19] Liang ZH, He WD. (2015). Research of supplier evaluation based on syncretized technique of neural network. Mathematics in Practice and Theory 45(24): 1-9.

[20] Yang YM, Wang YN. (2012). Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Transactions on Neural Networks and Learning Systems 23(9): 1498-1505. http://doi.org/10.1109/TNNLS.2012.2202289

[21] Wang YN, Yang YM. (2011). Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model. Measurement 44(8): 1389-1405. http://doi.org/10.1016/j.measurement.2011.05.010

[22] Huang GB, Zhu QY, Siew CK. (2006). Extreme learning machine: theory and applications. Neurocomputing. 70(1): 489-501. http://doi.org/10.1016/j.neucom.2005.12.126

[23] Minhas R, Mohammed AA, Wu QMJ. (2010). A fast recognition framework based on extreme learning machine using hybrid object information. Neurocomputing 73(10): 1831-1839. http://doi.org/10.1016/j.neucom.2009.11.049

[24] Zhang XY, Dou SQ. (2005). The assessment of ventilation system for underground mines based on neural network. Non-ferrous Mining and Metallurgy 21(4): 11-13.

[25] Huang GB, Ding XJ, Zhou HM. (2010). Optimization method based extreme learning machine for classification. Neurocomputing 74(1-3): 155-163. http://doi.org/10.1016/j.neucom.2010.02.019

[26] Yang YM. (2013). Researches on extreme learning theory for system identification and applications. Hunan University, Changsha.

[27] Guo J, Yue NF, Jin YF, Zheng XZ. (2016). Evaluation index system for rescue safety of mine thermodynamic disasters. Safety in Coal Mine 48(7): 253-256.