@article{YANG2019, 
author = {Liu YANG and Xue-ru WEN and Xiao-li WU and Li-xin PEI and Chen YUE and Bing LIU and Si-jia GUO},
title = {Height prediction of water flowing fractured zones based on BP artificial neural network},
year = {2019},
journal = {Journal of Groundwater Science and Engineering},
volume = {7},
number = {4},
pages = {354-359},
keywords = {Height of water flowing fractured zone, BP artificial neutral network, Comparative analysis},
url = {https://www.sciopen.com/article/10.19637/j.cnki.2305-7068.2019.04.006},
doi = {10.19637/j.cnki.2305-7068.2019.04.006},
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 applied 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 conditions 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.}
}