Network analysis of coal mine hazards based on text mining and link prediction

Ze Wang, Huajiao Li and Renwu Tang ()
Additional contact information
Ze Wang: School of Economics and Management, China University of Geosciences, Beijing 100083, P. R. China2Key Laboratory of Carrying Capacity, Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, P. R. China3Key Laboratory of Strategic Studies, Ministry of Natural Resources, Beijing 100082, P. R. China
Huajiao Li: School of Economics and Management, China University of Geosciences, Beijing 100083, P. R. China2Key Laboratory of Carrying Capacity, Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, P. R. China3Key Laboratory of Strategic Studies, Ministry of Natural Resources, Beijing 100082, P. R. China
Renwu Tang: School of Government, Beijing Normal University, Beijing 100083, P. R. China

International Journal of Modern Physics C (IJMPC), 2019, vol. 30, issue 07, 1-22

Abstract: Hazards are a potential source of harm and damage hiding in shadow zones. Without control, they may accumulate and interact with other types of hazards. In the harsh and complicated circumstance, especially, the deep underground space of coal mines, complex and nonlinear interactions among hazards multiply the probabilities that a hazard turns into accidents, more seriously, its effect may trigger more correlated hazards to worsen the accidents and bring huge loss of lives and assets. Therefore, identifying the correlations among hazards and understanding the complexity of interactions among coal mine hazards are significant for ensuring the safety of coal production. From this standpoint, we propose a hybrid method combing text mining and complex network method. First, we abstract the dangerous hazards from a large amount of text data. Then, we establish the coal mine hazard network (CMHN) to capture correlations among hazards. Finally, we analyze and predict the correlations among hazards based on CMHN. Through which, we find the fault-prone hazard and the recurrent hazards, more importantly, we figure out the nonlinear correlations among hazards and reveal the connection preference of hazards. Furthermore, we forecast the unknown correlations among hazards to take precautions of them. This study could be helpful for making prevention strategies for safety management in the coal mine and other highly dangerous industries.

Keywords: Complex network; text mining; hazard identification; LDA-Gibbs model; correlations prediction; safety management (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0129183119400096
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:ijmpcx:v:30:y:2019:i:07:n:s0129183119400096

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0129183119400096

Access Statistics for this article

International Journal of Modern Physics C (IJMPC) is currently edited by H. J. Herrmann

More articles in International Journal of Modern Physics C (IJMPC) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().