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Mine inflow prediction model based on unbiased Grey-Markov theory and its application

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Abstract

The accurate prediction of mine inflow plays an important role in the design of mine drainage capacity and the formulation of prevention and control measures for water disasters. Mine inflow is a dynamic system that is affected by many factors and is characterized by random fluctuation. The currently commonly used water inflow prediction methods often need more detailed mine geological data and complicated parameter solving process, and are limited in application. According to historical water inflow observation data of typical coal mines, a mine inflow prediction model based on unbiased grey and markov theory was established. Besides, the water inflow of a typical coal mine was predicted; and the accuracy was verified. The prediction results showed that: the mean relative error (MRE) between the predicted value of the mine inflow prediction model based on unbiased grey theory and the actual value was 3.85%. The MRE of the predicted value corrected by the markov model was improved to 2.42%. The data required for the mine inflow prediction model based on unbiased grey-markov theory are readily available. In addition, this model can eliminate the inherent bias of the traditional model and the effects of the random fluctuation of data on prediction results, and has higher computational accuracy. The relevant research results can provide some basis for the improvement of the mine inflow method.

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Acknowledgements

This research was financially supported by National Natural Science Foundation (42162022; 41702270), Guizhou Science and Technology Department Project (Qian Ke He Ji Chu [2019]1413; Qian Ke He Zhi Cheng [2020]4Y048; Qian Ke He Zhi Cheng [2020]4Y007), Guizhou Provincial Department of Education Foundation ([2018]113; Shanxi Province Coal Mine Water Hazard Prevention and Control Technology Open Fund (2020SKMS01).

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Authors and Affiliations

  1. Key Laboratory of Karst Georesources and Environment, Ministry of Education, Guizhou University, Guiyang, 550025, Guizhou, China

    Bo Li, Yulan Luo, Lei Liu & Teng Li

  2. Key Laboratory of Karst Environment and Geological Disaster Prevention and Control of Guizhou Province, Guizhou University, Guiyang, 550025, Guizhou, China

    Bo Li

  3. College of Resource and Environmental Engineering, Guizhou University, Guiyang, 550025, Guizhou, China

    Bo Li & Huiling Zhang

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  1. Bo Li
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  2. Huiling Zhang
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  3. Yulan Luo
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  4. Lei Liu
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  5. Teng Li
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Correspondence to Bo Li.

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Communicated by: H. Babaie

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Li, B., Zhang, H., Luo, Y. et al. Mine inflow prediction model based on unbiased Grey-Markov theory and its application. Earth Sci Inform 15, 855–862 (2022). https://doi.org/10.1007/s12145-022-00770-2

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