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    Data mining applications in evaluating mine ventilation system

    Access Status
    Fulltext not available
    Authors
    Cheng, Jianwei
    Yang, S.
    Date
    2012
    Type
    Journal Article
    
    Metadata
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    Citation
    Cheng, J. and Yang, S. 2012. Data mining applications in evaluating mine ventilation system. Safety Science. 50 (4): pp. 918-922.
    Source Title
    Safety Science
    DOI
    10.1016/j.ssci.2011.08.003
    ISSN
    0925-7535
    School
    Dept of Mining Eng & Metallurgical Eng
    URI
    http://hdl.handle.net/20.500.11937/41679
    Collection
    • Curtin Research Publications
    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. © 2011 Elsevier Ltd.

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