Mine Water and the Environment

, Volume 36, Issue 2, pp 217–225 | Cite as

Prediction Reliability of Water Inrush Through the Coal Mine Floor

Technical Article
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Abstract

Inrush of Ordovician limestone karst water through the mine floor occurs frequently in the Carboniferous-Permian coalfield in northern China. A probability index method was proposed to predict water inrushes using five indices: an aquifer water-bearing index, a structural index, an aquifuge index, an aquifer water pressure index, and an underground pressure index. Expert input was used to obtain weights for these five factors. Expert evaluation and statistical probability were then used to determine weights of the subsidiary factors, allowing the calculation of a water inrush probability index (I) and a threshold water inrush value for the Feicheng coalfield of 0.65. The Dempster-Shafer evidence theory was then used to determine a 74% degree of confidence for this prediction. Finally, the method was applied to the No. 9901 working face of the Taoyang coal mine. A subsequent 1,083 m3/h water inrush that occurred there aligned with the statistical results.

Keywords

Feicheng coalfield Water inrush probability index Multi-attribute decision D-S evidence theory Decision making model 

煤层底板突水概率预测

抽象

煤层底板奥陶系岩溶水突水是中国华北石炭-二叠纪煤田的主要煤矿水害类型。提出了一种包含含水层富水性指数、构造指数、隔水层指数、含水层水压指数和矿山压力指数的底板突水概率预测模型。通过专家打分法确定突水概率模型中五个指标的权重,采用专家评价和概率统计法获取次级指标的权重。肥城煤田突水概率指数临界值为0.65,Dempster-Shafer法证明该预测可信度为74%。底板突水概率预测模型应用于肥城煤田陶阳煤矿9901工作面底板突水预测,工作面开采期间突水量达1,083 m3/h,预测与实际揭露结果一致。

Prognosegenauigkeit von Liegendwassereinbrüchen in Kohlegruben

Zusammenfassung

Über die Grubenbausohle stattfindende Karstwassereinbrüche aus ordovizischen Kalksteinen sind im permokarbonischen Kohlerevier Nordchinas ein häufig auftretendes Phänomen. Zur Vorhersage von Einbruchsereignissen wird eine Wahrscheinlichkeitsindex-Methode vorgeschlagen, die auf fünf Einzelindizes beruht: Index der Grundwasserführung, Strukturindex, Grundwasserstauerindex, Grundwasserdruckindex, Gebirgsdruckindex. Die Ableitung der Wichtungsfaktoren der fünf Faktoren erfolgte anhand von Expertenschätzungen. Die Gewichtung weiterer sekundärer Faktoren wurde auf Basis von Sachverständigenbeurteilungen und statistischen Wahrscheinlichkeiten abgeleitet. Im Anschluss erfolgte die Berechnung des Wassereinbruchswahrscheinlichkeitsindex (I) und eines Wassereinbruchschwellenwerts für das Feicheng-Kohlerevier in Höhe von 0,65. Auf Basis der Dempster-Shafer-Theorie wurde für diese Vorhersage ein Zuverlässigkeitsgrad von 74 % ermittelt. Abschließend wurde die Methode für den Abbaustoß No. 9901 in der Taoyang-Kohlengrube angewandt. Ein nachfolgendes Wassereinbruchsereignis mit einem Zulauf von 1083 m3/h befand sich in Übereinstimmung mit den statistischen Ergebnissen.

Translator: Michael Paul

Confiabilidad de la predicción de irrupción de agua a través del piso de una mina de carbón

Resumen

Es frecuente la irrupción de agua a través de la caliza cárstica Ordovícica del piso de mina en el campo de carbón carbonífero-pérmico en el norte de China. Se propuso un método de índice de probabilidad para predicir las irrupciones de agua, usando cinco índices: un índice del contenido de agua en el acuífero, un índice estructural, un índice acuífugo, un índice de la presión de agua en el acuífero y un índice de presión subterránea. La opinión experta se utilizó para obtener los pesos de los cinco factores. La evaluación por expertos y la probabilidad estadística fueron usado para determinar los pesos de los factores subsidiarios, permitiendo el cálculo de un {inidce de probabilidad de irrupción de agua (I) y un valor límite para la irrupción de agua para el campo de carbón Feicheng de 0,65. La teoría Dempster-Shafer determinó 74% de grado de confianza en esta predicción. Finalmente el método fue aplicado a la cara de trabajo N° 9901 de la mina de carbón Taoyang. Una irrupción de agua de 1,083 m3/h que ocurrió posteriormente se ajustó a los resultados estadísticos.

Notes

Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (41572244), Ministry of Education Research Fund for the doctoral program (20133718110004), Shandong Province Nature Science Fund (ZR2015DM013), SDUST Research Fund (2012KYTD101), and the Taishan Scholars Construction projects. We thank the anonymous reviewers, the editors, and Mona Pelkey for their help in improving this manuscript.

Supplementary material

10230_2017_431_MOESM1_ESM.doc (40 kb)
Supplementary material 1 (DOC 39 KB)

References

  1. Altınçay H (2006) On the independence requirement in Dempster-Shafer theory for combining classifiers providing statistical evidence. Appl Intell 25:73–90. doi: 10.1007/s10489-006-8867-y CrossRefGoogle Scholar
  2. Donglin D, Wenjie S, Sha X (2012) Water-inrush assessment using a GIS-based Bayesian network for the 12–2 coal seam of the Kailuan Donghuantuo coal mine in China. Mine Water Environ 31:138–146. doi: 10.1007/s10230-012-0178-4 CrossRefGoogle Scholar
  3. Gong BG (2007) Based on evidence theory with incomplete information on attribute decision making method. University of Science and Technology of China, Hefei (in Chinese) Google Scholar
  4. Han LY, Zhou F (2006) Knowledge fusion based on D–S evidence theory and its application. J Beijing Univ Aeronaut Astronaut 32(1):65–68. doi: 10.13700/j.bh.1001-5965.2006.01.016 (Chinese) Google Scholar
  5. Han J, Zhu L, Cheng JL (2003) Software development for forecasting inundation in the coal mine. J Liaoning Tech Univ 22(5):589–591 (Chinese) Google Scholar
  6. Kang YH (1997) Data fusion theory and applications. Xi’an University of Electronic Science and Technology Press, Xi’an (in Chinese) Google Scholar
  7. Li B, Chen Y (2016) Risk assessment of coal floor water inrush from underlying aquifers based on GRA–AHP and its application. Geotech Geol Eng 34(1):143–154. doi: 10.1007/s10706-015-9935-z CrossRefGoogle Scholar
  8. Liu TM, Xia ZX, Xie HC (1998) Data fusion technology and its application. National Defense Industry Press, Beijing (Chinese) Google Scholar
  9. Mathon BR, Ozbek MM, Pinder GF (2010) Dempster–Shafer theory applied to uncertainty surrounding permeability. Math Geosci 42:293–307. doi: 10.1007/s11004-009-9246-0 CrossRefGoogle Scholar
  10. Meng Z, Li G, Xie X (2012) A geological assessment method of floor water inrush risk and its application. Eng Geol 143–144:51–60. doi: 10.1016/j.enggeo.2012.06.004 CrossRefGoogle Scholar
  11. Ministry of Coal Industry (2009) Regulations for mine water prevention and control. Beijing Publ House of Coal Industry, Beijing (in Chinese) Google Scholar
  12. Neshat A, Pradhan B (2015) Risk assessment of groundwater pollution with a new methodological framework: application of Dempster–Shafer theory and GIS. Nat Hazards 78:1565–1585. doi: 10.1007/s11069-015-1788-5 CrossRefGoogle Scholar
  13. Shafer G (1976) A mathematical theory of evidence. Princeton University press, PrincetonGoogle Scholar
  14. Shi LQ, Han J, Song Y, Zhang BP, Zhang XP, Bo CS (1999) Forecast of water inrush from mining floor with probability indexes. J Chin U Min Technol 28(5):442–444 (Chinese) Google Scholar
  15. Shi LQ, Qiu M, Wei WX, Xu DJ, Han J (2014) Water inrush evaluation of coal seam floor by integrating the water inrush coefficient and the information of water abundance. Int J Min Sci Technol 24(5):677–681. doi: 10.1016/j.ijmst.2014.03.028 CrossRefGoogle Scholar
  16. Shi LQ, Bo CS, Wei JC et al (2015) Control theory and technology of ordovician limestone karst water inrush in north china coalfield. China Coal Industry Publishing House, Beijing (Chinese) Google Scholar
  17. Sun R, Sun SY, Ge YF (2006) How to obtain the basic probability evaluates in D–S theory. Mod Mach 4:22–23. doi: 10.13667/j.cnki.52-1046/th.2006.04.010 Google Scholar
  18. Valipour M (2014) Analysis of potential evapotranspiration using limited weather data. Appl Water Sci. doi: 10.1007/s13201-014-0234-2 (press, online first) Google Scholar
  19. Valipour M (2015a) Future of agricultural water management in Africa. Arch Agron Soil Sci 61(7):907–927. doi: 10.1080/03650340.2014.961433 CrossRefGoogle Scholar
  20. Valipour M (2015b) Long-term runoff study using SARIMA and ARIMA models in the United States. Met Apps 22:592–598. doi: 10.1002/met.1491 CrossRefGoogle Scholar
  21. Valipour M, Montazar AA (2012a) Optimize of all effective infiltration parameters in furrow irrigation using visual basic and genetic algorithm programming. Aust J Basic Appl Sci 6(6):132–137Google Scholar
  22. Valipour M, Montazar AA (2012b) Sensitive analysis of optimized infiltration parameters in SWDC model. Adv Environ Biol 6(9):2574–2581Google Scholar
  23. Valipour M, Banihabib ME, Behbahani SMR (2012) Parameters estimate of autoregressive moving average and autoregressive integrated moving average models and compare their ability for inflow forecasting. J Math Stat 8(3):330–338CrossRefGoogle Scholar
  24. Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441. doi: 10.1016/j.jhydrol.2012.11.017 CrossRefGoogle Scholar
  25. Wang YC, Wang HF, Yang CW (2005) Method of implementation of fusing correlated attribute information in target recognition. Ordnance J 26(3):338–342 (in Chinese) Google Scholar
  26. Wu Q, Zhou W (2008) Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: vulnerability index method and its construction. Environ Geol 55(4):245–254. doi: 10.1007/s00254-007-1160-5 CrossRefGoogle Scholar
  27. Wu W, Zhang HB, Wang DX (2005) Study of flood prediction based on multi-evidential fusion model. Hydroelectr Power 31(12): 22–24 (in Chinese) Google Scholar
  28. Wu Q, Xu H, Pang W (2008) GIS and ANN coupling model: an innovative approach to evaluate vulnerability of karst water inrush in coalmines of north China. Environ Geol 54:937–943. doi: 10.1007/s00254-007-0887-3 CrossRefGoogle Scholar
  29. Wu Q, Liu Y, Liu D, Zhou W (2011) Prediction of floor water inrush: the application of GIS-based AHP vulnerable index method to Donghuantuo coal mine, China. Rock Mech Rock Eng 44:591–600. doi: 10.1007/s00603-011-0146-5 CrossRefGoogle Scholar
  30. Yang G, Wu X (2007) Synthesized fault diagnosis method based on fuzzy logic and D–S evidence theory. Adv Intell Comput Theor Appl Lect Notes Comp Sci 4682:1024–1031. doi: 10.1007/978-3-540-74205-0_106 CrossRefGoogle Scholar
  31. Zhu CL (2005) Urban water environmental system control decision support technology research. Hohai University, Nanjing (in Chinese) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.College of Earth Sciences and EngineeringShandong University of Science and TechnologyQingdaoChina
  2. 2.Shandong Provincial Key Laboratory of Depositional Mineralization and Sedimentary MineralsQingdaoChina
  3. 3.College of Information Science and EngineeringShandong University of Science and TechnologyQingdaoChina

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