Data mining applications in evaluating mine ventilation system
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...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
2012
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| Online Access: | http://hdl.handle.net/20.500.11937/41679 |
| _version_ | 1848756212298940416 |
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| author | Cheng, Jianwei Yang, S. |
| author_facet | Cheng, Jianwei Yang, S. |
| author_sort | Cheng, Jianwei |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T09:08:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-41679 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:08:36Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-416792017-09-13T14:15:05Z Data mining applications in evaluating mine ventilation system Cheng, Jianwei Yang, S. 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. 2012 Journal Article http://hdl.handle.net/20.500.11937/41679 10.1016/j.ssci.2011.08.003 restricted |
| spellingShingle | Cheng, Jianwei Yang, S. Data mining applications in evaluating mine ventilation system |
| title | Data mining applications in evaluating mine ventilation system |
| title_full | Data mining applications in evaluating mine ventilation system |
| title_fullStr | Data mining applications in evaluating mine ventilation system |
| title_full_unstemmed | Data mining applications in evaluating mine ventilation system |
| title_short | Data mining applications in evaluating mine ventilation system |
| title_sort | data mining applications in evaluating mine ventilation system |
| url | http://hdl.handle.net/20.500.11937/41679 |