Skip to main content
SpringerLink
Log in

Recognition model of groundwater inrush source of coal mine: a case study on Jiaozuo coal mine in China

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

The mine water source discrimination plays an important role in guiding mine water prevention in the water prevention work. Improving especially the discrimination accuracy of mine water sources which will cause water inrush is the important foundation of avoiding such accident and thus ensuring personnel and property being in safety. Based on Fisher discriminant analysis theory (FDA) and gray correlation analysis theory (GCA), groundwater chemical components (Ca2+, Mg2+, K++Na+, Cl−, SO4 2−, and HCO3 −) data at main water inrush aquifers in a typical coal mine through experiments, FDA-GCA recognition model of water inrush sources was established and then verified. Results indicated that the FDA-GCA recognition model of water inrush sources was characterized by high discrimination precision, and false determination rate obtained by back-substitution estimation method was zero. Thus, it had strong discrimination ability of water inrush sources. The significance ranking of input variables in the water source FDA-GCA recognition model was Ca2+ > Mg2+ > HCO3 − > SO4 2− > K+ + Na+ > Cl−. The data in this paper were discriminated by the distance discriminant method and BP neural network discriminant analysis method, the correct rate of which is 90%, 80%, slightly lower than or equal to the accuracy of 90% by the FDA-GCA recognition model.

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

Similar content being viewed by others

References

  • Cao B, Bai G, Li H (2015) Prediction of gas content based on PCA-GA-BP neural network. J Saf Sci Technol 11(5):84–90

    Google Scholar 

  • Chen HJ, Li XB, Liu AH et al (2009) Identifying of mine water inrush sources by Fisher discriminant analysis method. J Cent South Univ 40(4):1114–1120

    Google Scholar 

  • Chen JH, Zheng RK, Chen H (2014) Analysis on slope stability based on combination of CA and BP neural network. China Saf Sci J 10(5):142–147

    Google Scholar 

  • Guo Q (2013) Study on water inrush of limestone aquifer in Lvliang coal mine. J Saf Sci Technol 9(6):16–20

    Google Scholar 

  • Hu JX, Zhang GJ (2013) K-fold cross-validation based selected ensemble classification algorithm. Bull Sci Technol 29(12):115–117

    Google Scholar 

  • Huang PH, Chen JS (2011) Fisher identify and mixing model based on multivariate statistical analysis of mine water inrush sources. J China Coal Soc 36:131–136

    Google Scholar 

  • Huang P, Han S (2016) Assessment by multivariate analysis of groundwater–surface water interactions in the Coal-mining Exploring District, China[J]. Earth Sci Res J 20(1):G1–G8

    Google Scholar 

  • Jing CL, Jiang HQ (2012) Prediction of karst collapse based on Fisher discriminant analysis method. J Earth Sci Environ 34(1):91–95

    Google Scholar 

  • Kanti KM, Rao PS (2008) Prediction of bead geometry in pulsed GMA welding using back propagation neural network. J Mater Process Technol 200:300–305

    Article  Google Scholar 

  • Keskin TE, Düğenci M, Kaçaroğlu F (2015) Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey). Environ Earth Sci 73:5333–5347

    Article  Google Scholar 

  • Li Y, Sun YJ, Xu ZM et al (2010a) Analysis of composition of mine inflow from complicated Multi Aquifer affecting safety production in coal mines. J Min Saf Eng 279(3):433–437

    Google Scholar 

  • Li Y, Xu ZM, Liu Y (2010b) Summary on methods of distinguishing sources of mine water-invasion. Coal Technol 29(11):87–89

    Google Scholar 

  • Liang YT, Yang ZH, Qu LW (2013) Mine roof fall prediction technique based on multi-source information fusion. J Xi’ an Univ Sci Technol 33(1):78–83

    Google Scholar 

  • Lu JT, Li XB, Gong FQ et al (2012) Recognizing of mine water inrush sources based on principal components analysis and Fisher discrimination analysis method. Chinese Saf Sci J 79:109–115

    Google Scholar 

  • Nie FQ, Xu GQ, Gan WJ et al (2013) Application of Ma Distance discriminant model on water source identification of mine water inrush. Ground Water 35(6):41–42

    Google Scholar 

  • Qian JZ, Lv C, Zhao WD et al (2010) Application of Elman and BP neural networks in discriminating water bursting source of coalmine. Syst Eng Theory Pract 30(1):145–150

    Google Scholar 

  • Richard A, Johnson DWW (2002) Applied multivariate statistical analysis[M]. [s.1]:Prentice Hall

  • Wang XY, Xu T, Huang D (2011) Application of distance discriminance in identifying water inrush resource in similar coalmine. J China coal soc 36(8):1354–1358

    Google Scholar 

  • Wang JY, Li ZY, Zhang XQ et al (2013) Discrimination of water bursting source in mine based on radial basis function neural network optimized by particle swarm optimization. Saf Environ Eng 20(5):118–121

    Google Scholar 

  • Wen TX, Zhang B, Shao LS (2014) QGA—LSSVM model for mine water inrush source identification. J Saf Sci Technol 24(7):111–116

    Google Scholar 

  • Wu JW, Xu SP, Zhai XR et al (2015) Hydrochemical characteristics and water sources discrimination of coal bearing sandstone water in the north eighth mining area of Taoyuan coal mine in Huaibei. J Saf Sci Technol 11(1):84–90

    Google Scholar 

  • Xu ZJ, Yang YG, Tang L (2007) Application of BP neural network in evaluation of water source in mine. Saf Coal Mines 38(2):4–6

    Google Scholar 

  • Xu X, Guo BB, Wang GZ (2016) Application of artificial neural network in recognition mine multiple water sources. J Saf Sci Technol 12(1):181–185

    Google Scholar 

  • Yan ZG, Bai HB (2009) MMH support vector machines model for recognizing Multi-Headstream of water inrush in mine. Chin J Rock Mech Eng 28(2):324–329

    Google Scholar 

  • Yang YG, Huang FC (2007) Water source determination of mine inflow based on nonlinear method. J China Univ Min Technol 36(3):283–286

    Google Scholar 

  • Yang HJ, Wang GC (2012) Summarization of methods of distinguishing sources and forecasting inflow of water inrush in coal mines. Coal Geol Explor 40(3):48–54

    Google Scholar 

  • Yang FQ, Liu GN, Guo LL (2015) Improved SVM and GA-BP neural network model of mine water inrush sources identification. Nonferrous Mets (Mining Section) 67(1):87–91

    Google Scholar 

  • Zhang XL, Zhang ZX, Peng SP (2003) Application of the second theory of quantification in identifying gushing water sources of coal mines. J China Univ Min Technol 32(3):251–254

    Google Scholar 

  • Zhang SJ, Zhu RJ, Jiang CL et al (2013) Prediction model for gas emission quantity in mining face based on partial leastsquares regression. Saf Coal Mines 44(2):7–11

    Google Scholar 

  • Zhao LN (2013) Research and application of Fisher discriminant method[D].Harbin:Northeast Forestry University

  • Zhu C, Qian JZ, Zhou XP et al (2010) Water inrush source discrimination with BP neural network in Pansan Mine. J Anhui Inst Archit Ind (Nature Science Edition) 18(5):35–38

    Google Scholar 

Download references

Acknowledgements

This work was financially supported by China Postdoctoral foundation project (Grant Nos. 2017M612395) and the Technological Innovation Team of Colleges and Universities in Henan Province (Grant No. 15IRTSTHN027).

Author information

Authors and Affiliations

  1. School of Resources and Environment, Henan Polytechnic University, Jiaozuo, 454000, China

    Huang Ping-hua & Wang Xin-yi

  2. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo, 454000, China

    Han Su-min

Authors
  1. Huang Ping-hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wang Xin-yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Han Su-min
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huang Ping-hua.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ping-hua, H., Xin-yi, W. & Su-min, H. Recognition model of groundwater inrush source of coal mine: a case study on Jiaozuo coal mine in China. Arab J Geosci 10, 323 (2017). https://doi.org/10.1007/s12517-017-3099-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-017-3099-5

Keywords

Search

Navigation