Height prediction of water flowing fractured zones based on BP artificial neural network |
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| Authors: | YANG Liu WEN Xue-ru WU Xiao-li PEI Li-xin YUE Chen LIU Bing GUO Si-jia |
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| Institution: | 1.China University of Mining & Technology (Beijing), Beijing 10083, China.;2.Institute of Hydrogeology and Environmental Geology, CAGS, Shijiazhuang 050061, China.;3.Beijing Geological and Mineral Exploration and Development Corporation, Beijing 10050, China. |
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| Abstract: | Factures caused by deformation and destruction of bedrocks over coal seams can easily lead to water flooding (inrush) in mines, a threat to safety production. Fractures with high hydraulic conductivity are good watercourses as well as passages for inrush in mines and tunnels. An accurate height prediction of water flowing fractured zones is a key issue in today's mine water prevention and control. The theory of leveraging BP artificial neural network in height prediction of water flowing fractured zones is analysed and app-lied in Qianjiaying Mine as an example in this paper. Per the comparison with traditional calculation results, the BP artificial neural network better reflects the geological condi-tions of the research mine areas and produces more objective, accurate and reasonable results, which can be applied to predict the height of water flowing fractured zones. |
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| Keywords: | Height of water flowing fractured zone BP artificial neutral network Comparative analysis |
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