LAUSR

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.