Data Mining Applications in Evaluating Mine Ventilation System
A mines 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.
Mine ventilation Date mining Early warning model Rough set Support Vector Machine
J.Cheng S.Yang
Depaartment of Mining Engineering, West Virginia University,Morgantwon,WV,26506,U.S.A State Key of Laboratory of Mine Resource and Safety Exploition, China University of Mining and Techn
国际会议
The First International Symposium on Mine Safety Science and Engineering (首届矿山安全科学与工程学术会议)
北京
英文
327-335
2011-10-27（万方平台首次上网日期，不代表论文的发表时间）