Table of Contents Author Guidelines Submit a Manuscript
Geofluids
Volume 2018, Article ID 9205025, 10 pages
https://doi.org/10.1155/2018/9205025
Research Article

Piper-PCA-Fisher Recognition Model of Water Inrush Source: A Case Study of the Jiaozuo Mining Area

1School of Resources and Environment Engineering, Henan Polytechnic University, Henan, Jiaozuo 454000, China
2Collaborative Innovation Center of Coalbed Methane and Shale Gas for Central Plains Economic Region, Henan, Jiaozuo 454000, China

Correspondence should be addressed to Pinghua Huang; nc.ude.uph@1002hph

Received 12 September 2017; Revised 14 January 2018; Accepted 30 January 2018; Published 26 February 2018

Academic Editor: Cinzia Federico

Copyright © 2018 Pinghua Huang and Xinyi Wang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. P.-H. Huang, J.-S. Chen, and C. Ning, “The analysis of hydrogen and oxygen isotopes in the ground water of Jiaozuo mine area,” Journal of the China Coal Society, vol. 37, no. 5, pp. 770–775, 2012. View at Google Scholar · View at Scopus
  2. G. Y. Pan, S. N. Wang, and X. Y. Sun, “Application of isotopic technique in identification of mine water inrush source,” Mining Safety & Environmental Protection, vol. 2008, no. 4, pp. 7–9, 2009. View at Google Scholar
  3. Z. Chen, D. Li, and F. Jiang, “The prediction model of ground water inrush from floor in jiaozuo coal mine,” Coal Geology & Exploration, vol. 4, pp. 38–40, 1996. View at Google Scholar
  4. X. Xu, B. Guo, and G. Z. Wang, “Application of artificial neural network for recognition of multiple water sources in mine,” Journal of Safety Science and Technology, vol. 12, no. 1, pp. 181–185, 2016. View at Google Scholar
  5. M. Qiu, L. Shi, C. Teng, and Y. Zhou, “Assessment of water inrush risk using the fuzzy delphi analytic hierarchy process and grey relational analysis in the liangzhuang Coal Mine, China,” Mine Water and the Environment, vol. 36, no. 1, pp. 39–50, 2017. View at Publisher · View at Google Scholar · View at Scopus
  6. T. E. Keskin, M. Düğenci, and F. Kaçaroğlu, “Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartın (Turkey),” Environmental Earth Sciences, vol. 73, no. 9, pp. 5333–5347, 2015. View at Publisher · View at Google Scholar · View at Scopus
  7. G. Li, Z. Meng, X. Wang, and J. Yang, “Hydrochemical prediction of mine water inrush at the Xinli Mine, China,” Mine Water and the Environment, vol. 36, no. 1, pp. 78–86, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. Y. Wang, M.-R. Zhou, P.-C. Yan, C.-Y. He, and D. Liu, “Identification of coalmine water inrush source with pca-bp model based on laser-induced fluorescence technology,” Spectroscopy and Spectral Analysis, vol. 37, no. 3, pp. 978–983, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Wu, S. Xu, R. Zhou, and Y. Qin, “Scenario analysis of mine water inrush hazard using Bayesian networks,” Safety Science, vol. 89, pp. 231–239, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. Q. Wu, Y. Liu, X. Wu, S. Liu, W. Sun, and Y. Zeng, “Assessment of groundwater inrush from underlying aquifers in Tunbai coal mine, Shanxi province, China,” Environmental Earth Sciences, vol. 75, no. 9, article no. 737, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. P.-C. Yan, M.-R. Zhou, Q.-M. Liu, R. Wang, and J. Liu, “Research on the source identification of mine water inrush based on LIF technology and PLS-DA algorithm,” Spectroscopy and Spectral Analysis, vol. 36, no. 9, pp. 2858–2862, 2016. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Qian, L. Wang, L. Ma, Y. Lu, W. Zhao, and Y. Zhang, “Multivariate statistical analysis of water chemistry in evaluating groundwater geochemical evolution and aquifer connectivity near a large coal mine, Anhui, China,” Environmental Earth Sciences, vol. 75, no. 9, article no. 747, 2016. View at Publisher · View at Google Scholar · View at Scopus
  13. P.-H. Huang and J.-S. Chen, “Fisher indentify and mixing model based on multivariate statistical analysis of mine water inrush sources,” Journal of the China Coal Society, vol. 36, no. S1, pp. 131–136, 2011. View at Google Scholar · View at Scopus
  14. J. T. Lu, X. B. Li, and F. Q. Gong, “Recognizing of mine water inrush sources based on principal components analysis and fisher discrimination analysis method,” China Safety Science Journal, vol. 22, no. 7, pp. 109–115, 2012. View at Google Scholar
  15. H. Chen J, X. B. Li, A. H. Liu et al., “Identifying of mine water inrush sources by Fisher discriminant analysis method,” Journal of Central South University, vol. 40, pp. 1114–1120, 2009. View at Google Scholar
  16. M. Alizamir and S. Sobhanardakani, “A comparison of performance of artificial neural networks for prediction of heavy metals concentration in groundwater resources of toyserkan plain,” Avicenna Journal of Environmental Health Engineering, vol. 4, no. 1, 2017. View at Publisher · View at Google Scholar
  17. J. S. Alagha, M. A. M. Said, and Y. Mogheir, “Modeling of nitrate concentration in groundwater using artificial intelligence approach-a case study of Gaza coastal aquifer,” Environmental Modeling & Assessment, vol. 186, no. 1, pp. 35–45, 2014. View at Publisher · View at Google Scholar · View at Scopus
  18. I. N. Daliakopoulos, P. Coulibaly, and I. K. Tsanis, “Groundwater level forecasting using artificial neural networks,” Journal of Hydrology, vol. 309, no. 1–4, pp. 229–240, 2005. View at Publisher · View at Google Scholar · View at Scopus
  19. X.-Y. Wang, T. Xu, and D. Huang, “Application of distance discriminance in identifying water inrush resource in similar coalmine,” Journal of the China Coal Society, vol. 36, no. 8, pp. 1354–1358, 2011. View at Google Scholar · View at Scopus
  20. X. D. Pen, Y. H. Guo, Y. W. Jie et al., “Application and discussion of fuzzy comprehensive evaluation in identifying mine inrush water source,” Mining safety and environmental protection, vol. 33, no. 3, pp. 57–59, 2006. View at Google Scholar
  21. Y. G. Yang, B. T. Li, and K. Q. Shang, “Fuzzy comprehensive evaluation of water gush and its prediction in coal mines of hebi mining bureau,” Journal of China University of Mining & Technology, vol. 27, no. 2, pp. 204–208, 1998. View at Google Scholar
  22. Z.-G. Yan, P.-J. Du, and D.-Z. Guo, “SVM models for analysing the headstreams of mine water inrush,” Journal of the China Coal Society, vol. 32, no. 8, pp. 842–847, 2007. View at Google Scholar · View at Scopus
  23. Z. J. Xu, Y. G. Yang, and L. Tang, “Application of BP neural network in evaluation of water source in mine,” in Safety in Coal Mines, pp. 4–6, 2 edition, 2007. View at Google Scholar
  24. X. L. Zhang, Z. X. Zhang, and S. P. Peng, “Application of the second theory of quantification in identifying gushing water sources of coal mines,” Journal of China University of Mining & Technology, vol. 32, no. 3, pp. 251–254, 2003. View at Google Scholar
  25. I. M. Farnham, K. J. Stetzenbach, A. K. Singh, and K. H. Johannesson, “Deciphering groundwater flow systems in Oasis Valley, Nevada, using trace element chemistry, multivariate statistics, and geographical information system,” Mathematical Geology, vol. 32, no. 8, pp. 943–968, 2000. View at Publisher · View at Google Scholar · View at Scopus