Application of Machine Learning to Determine Underground Hazard Location

Details

• Personal Author:
  Bahrami D ; Xue Y ; Yuan L ; Zhou L
• Description:
  Underground mine accidents, such as mine fires, remain a concern for mine operators, posing a health and safety risk to the mine workers. Dealing with an unknown location of an accident underground can be a challenging task, creating a hazardous condition for miners during an evacuation and rescue operation. A timely determination of an underground fire event's location and size is of great importance in reducing the risk of any injuries. Machine learning (ML) has made its way into mining, enabling the development of data-driven predictive models that can be applied to miner's health and safety problems. A new methodology has been developed using the application of a ML technique to characterize underground accidents such as size and location of an underground mine fire using the post-fire airflow data. This paper describes the methodology and its verification through examples. The National Institute for Occupational Safety and Health (NIOSH) is endeavoring to develop workplace solutions to improve detection of and reduce the risk of hazardous conditions. The results demonstrate a promising application of the ML-based models using the airflow monitoring and provide a useful tool for solving the problem of unknown fire location and reducing the risk of hazardous conditions. [Description provided by NIOSH]
• Subjects:
  Air Movements Mining Occupational Health Safety Software Ventilation
• Keywords:
  Air Flow Computer Models Fire Detection Systems Mine Disasters Mine Fires Mine Safety Mining Equipment Mining Industry Underground Mining
• Series:
  Mining Publications
• ISBN:
  9781032036793
• Publisher:
  CRC Press
• Document Type:
  Text
• Genre:
  Proceedings
• Place as Subject:
  OSHA Region 3 ; Pennsylvania
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  PMRD - Pittsburgh Mining Research Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  401-409
• NIOSHTIC Number:
  nn:20063605
• Citation:
  Mine Ventilation: Proceedings of the 18th North American Mine Ventilation Symposium (NAMVS 2021), June 12-17, 2021, Rapid City, South Dakota. Tukkaraja P ed. London: CRC Press, 2021 Jun; :401-409
• Contact Point Address:
  Davood Bahrami, Pittsburgh Mining Research Division, National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, USA
• Editor(s):
  Tukkaraja P
• Federal Fiscal Year:
  2021
• NORA Priority Area:
  Mining
• Peer Reviewed:
  False
• Source Full Name:
  Mine Ventilation: Proceedings of the 18th North American Mine Ventilation Symposium (NAMVS 2021), June 12-17, 2021, Rapid City, South Dakota
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:ce183dc00f464adf34b62cbfb1b41b7cb58c69fee16469938fd4fdb4e1c695a8327f5df6e6913f14ae11e5ef79de6da018d739abad94cee4acc047e3ac770904
• Download URL:
  https://stacks.cdc.gov/view/cdc/215450/cdc_215450_DS1.pdf
• File Type:
  [PDF - 639.43 KB ]