Research on Piper-PCA-Bayes-LOOCV discrimination model of water inrush source in mines

  • Pinghua Huang
  • Zhongyuan YangEmail author
  • Xinyi Wang
  • Fengfan Ding
Review Paper


The occurrence of mine water inrush constantly threatens the safety production of coal mines and causes serious financial losses in China. Existing water inrush source identification methods do not consider the mixing effect of aquifers and the complexity of discrimination indexes. To identify water inrush rapidly and accurately, an identification model that combines water chemistry and multivariate statistical methods is proposed. The Piper trilinear diagram was used to screen 48 water samples taken from a water inrush aquifer in a mining area. Forty-two typical water samples that represent the water inrush aquifers were obtained. They were selected as training samples with discrimination indexes (Ca2+, Mg2+, Na++K+, HCO3, SO42−, Cl). Principal component analysis (PCA) was used to extract three principal indexes. Then, with these indexes taken as factors of Bayesian discrimination, we established a model for determining the source of water bursting in Pingdingshan Mine. Finally, the prejudgment classification stability of the constructed model was evaluated by leave-one-out cross-validation (LOOCV), which reports 95.2% overall classification accuracy. The constructed model was used to obtain a prognosis of 10 samples collected from Pingdingshan Mine, and it reported one misjudgment on one sample. In addition to comparing the prediction results with fuzzy comprehensive evaluation and gray relational degree model, results indicate that the constructed Piper-PCA-Bayes-LOOCV discrimination model of the water inrush source in mines can increase the recognition accuracy effectively, thus guaranteeing the safety production of mines.


Water bursting source Piper trilinear diagram Principal component analysis Piper-PCA-Bayes-LOOCV discrimination model Leave-one-out cross-validation 


Funding information

This work was financially supported by the National Natural Science Foundation of China (Grant 41672240), Science and Technology Key Research Project of the Education Department of Henan, China (nos. 13A170313, 14A510022), China Postdoctoral Science Foundation (2017M612395), Innovation Scientists and Technicians Troop Construction Projects of Henan Province (Grant CXTD2016053), Henan Province’s Technological Innovation Team of Colleges and Universities (Grant 15IRTSTHN027), Fundamental Research Funds for the Universities of Henan Province (NSFRF1611), and Scientists and Technicians Projects of Henan Province (Grant 182107000019).


  1. Alizamir M, Sobhanardakani S (2017) A comparison of performance of artificial neural networks for prediction of heavy metals concentration in groundwater resources of toyserkan plain. Avicenna J Environ Health Eng 4(1):11792.
  2. Chang QL, Sun XK, Zhou HQ et al (2018) A multivariate matrix model of analysing mine water bursting and its application. Desalin Water Treat 123:20–26. CrossRefGoogle Scholar
  3. Deng WP, Yu XX, Jia GD et al (2013) Comparison of the methods using stable hydrogen and oxygen isotope to distinguish the water source of Quercus variabilis in dry season. J Basic Sci Eng 21(3):412–422 (in Chinese).
  4. Deng QH, Cao JY, Zhang LP et al (2014) The Bayesian discrimination model for sources of mine water inrush based on principal components analysis. Hydrogeology & Engineering Geology 41(6):20–25(in Chinese). CrossRefGoogle Scholar
  5. Faghih Nasiri E, Yousefi Kebria D, Qaderi F (2018) An experimental study on the simultaneous phenol and chromium removal from water using titanium dioxide photocatalyst. Civil Eng J 4(3):585. CrossRefGoogle Scholar
  6. Fan YT, Chen YN, He Q (2016) Isotopic characterization of river waters and water source identification in an Inland River, Central Asia. Water 8(7):286. CrossRefGoogle Scholar
  7. Gong FQ, Lu JT (2014) Recognition method of mine water inrush sources based on the principal element analysis and distance discrimination analysis. J Min Saf Eng 31(2):236–242 (in Chinese).
  8. Gümrah F, Öz B, Güler B, Evin S (2000) The application of artificial neural networks for the prediction of water quality of polluted aquifer. Water Air Soil Pollut 119(1):275–294. CrossRefGoogle Scholar
  9. Huang PH, Wang XY (2018) Piper-PCA-Fisher recognition model of water inrush source: a case study of the Jiaozuo mining area. Geofluids 2018:10. CrossRefGoogle Scholar
  10. Huang PH, Chen JS, Ning C (2012) The analysis of hydrogen and oxygen isotopes in the ground water of Jiaozuo mine area. J China Coal Soc 37(5):770–775(in Chinese. CrossRefGoogle Scholar
  11. Jiang XY, Cheng CQ (2009) Hydrochemical classification and identification of groundwater in mining region using multivariate statistical analysis. Hydrogeol Eng Geol 36(4):16–20 (in Chinese.
  12. Li JX, Hu QT (2007) Application of principal component analysis in coal mine safety evaluation. Min Saf Environ Prot 34(5):71–76(in Chinese.
  13. Li B, Shi HB, Li Z (2015) An analyzing method for chemical classifications of groundwater based on the Bayes discriminant theory. Agric Res Arid Areas 33(4):246–250(in Chinese. CrossRefGoogle Scholar
  14. Liu WT, Song CW, Zhang GY (2002) Stratification analysis and prediction on floor water invasion by experts. Geotech Invest Surv (1):22–25 (in Chinese).
  15. Liu MC, Chen SS, Qian JZ (2014) Bayesian method in the application of discriminant of water gushing source in Dingji coal mine. International Conference on Environmental Technology and Knowledge Transfer.
  16. Liu Q, Sun YJ, Xu ZM (2018) Application of the comprehensive identification model in analyzing the source of water inrush. Arab J Geosci 11(9):189. CrossRefGoogle Scholar
  17. Lu JT, Li XB, Gong FQ (2012) Recognizing of mine water inrush sources based on principal components analysis and fisher discrimination analysis method. China Saf Sci J 22(7):109–115(in Chinese.
  18. Lv RS, Li B, Xu YY (2011) Fuzzy comprehensive evaluation of groundwater pollution in coal mining area. 2011 International Symposium on Water Resource and Environmental Protection.
  19. Qiu M, Shi L, Teng C, Zhou Y (2017) Assessment of water inrush risk using the fuzzy delphi analytic hierarchy process and grey relational analysis in the Liangzhuang coal mine, China. Mine Water Environ 36(1):39–50. CrossRefGoogle Scholar
  20. Wang XY, Ji HY, Wang Q et al (2016) Divisions based on groundwater chemical characteristics and discrimination of water inrush sources in the Pingdingshan coalfield. Environ Earth Sci 75(10).
  21. Wang Y, Zhou MR, Yan PC et al (2017) Identification of coalmine water inrush source with PCA-BP model based on laser-induced fluorescence technology. Spectrosc Spectr Anal 37(3):978–983. CrossRefGoogle Scholar
  22. Wei WX, Han J, Shi LQ et al (2015) Application of modern data analysis in mine water gushing prediction. Coal Industry Press, BeijingGoogle Scholar
  23. Wu Q, Mu WP, Xing Y et al (2019) Source discrimination of mine water inrush using multiple methods: a case study from the Beiyangzhuang Mine, Northern China. Bull Eng Geol Environ 78(1):469–482. CrossRefGoogle Scholar
  24. Xue Y, Song JX, Zhang Y (2016) Nitrate pollution and preliminary source identification of surface water in a semi-arid river basin, using isotopic and hydrochemical approaches. Water 8(8):328. CrossRefGoogle Scholar
  25. Yan DD, Ma L, Qian JZ et al (2010) Water inrush source of Xinzhuangzi coal mine identified with gray related degree method. Proceedings of the 3rd International Conference on Environmental Technology and Knowledge Transfer.
  26. Yan PC, Zhou MR, Liu QM (2016) Research on the source identification of mine water inrush based on LIF technology and PLS-DA algorithm. Spectrosc Spectr Anal 36(9):2858–2862. CrossRefGoogle Scholar
  27. Yang LY, Xu ZS et al (2019) Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning. Int J Mach Learn Cybern 10(3):591–601. CrossRefGoogle Scholar
  28. Zhang XL, Zhang ZR, Peng SP (2003) Application of the second theory of quantification in identifying rushing water sources of coal mines. J China Univ Min Technol 32(3):251–254(in Chinese. CrossRefGoogle Scholar
  29. Zhang H, Yao DX, Lu HF et al (2017) Application of principal component analysis and Bayes discrimination approach in water source identification. Coal Geol Explor 45(5):87–93(in Chinese.

Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  • Pinghua Huang
    • 1
    • 2
  • Zhongyuan Yang
    • 1
    Email author
  • Xinyi Wang
    • 1
    • 2
  • Fengfan Ding
    • 1
  1. 1.School of Resources and Environment EngineeringHenan Polytechnic UniversityJiaozuoChina
  2. 2.Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic RegionJiaozuoChina

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