Citation

Cheng J, Yang S-Q. Safety Sci. 2012; 50(4): 918-922.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.ssci.2011.08.003

PMID

unavailable

Abstract

A mine's ventilation system is an important component of an underground mining system. It provides a sufficient quantity of air to maintain suitable working environment. Therefore, the status of mine ventilation should be tracked and monitored as a timely matter. Based on former findings and in-depth analysis of mine ventilation systems, a proper early warning model is proposed in this paper for such considerations to improve the mine ventilation safety. The model itself is comprised of two sub-models, and two data mining techniques are used to assist in building each sub-model. One is the optimal indexes selection model which applies the Rough Set theory (RS) to assist the selection of best ventilation indexes. The other is the risk evaluation model based on the Support Vector Machine (SVM) to classify the risk ranks for the mine ventilation system. Testing cases have been used to demonstrate the applicability of this integrated model.