Characterizing Fire in Large Underground Ventilation Networks Using Machine Learning

Bahrami D; Xue Y; Yuan L; Zhou L

i

Characterizing Fire in Large Underground Ventilation Networks Using Machine Learning

Public Domain

2024/02/25
By Bahrami D ; Xue Y ; Yuan L ; ...
Series: Mining Publications

Details

Personal Author:

Bahrami D ; Xue Y ; Yuan L ; Zhou L
Description:

Underground mine accidents, such as mine fires, remain a health and safety risk to mine workers. Researchers at the National Institute for Occupational Safety and Health (NIOSH) are developing a data-driven, predictive model for characterizing the location and size of unknown underground fires. This study examines applying a machine learning-based model to predict fire size and location in a large underground metal mine based on hypothetical scenarios on the model performance. The results show that the size and location of an unknown fire can be determined with over 80% and 90% accuracy, respectively, and potentially help to reduce the risk of hazardous conditions for emergency response. [Description provided by NIOSH]
Subjects:

Fires Mining Occupational Health Safety Ventilation
Keywords:

Fire Hazards Machine Learning Mine Fires Mine Rescue Mining Industry Underground Mines Ventilation Systems
Series:

Mining Publications
Publisher:

Society for Mining, Metallurgy & Exploration (SME)
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:

1-6
NIOSHTIC Number:

nn:20069434
Citation:

MineXchange: 2024 SME Annual Conference & Expo, February 25-28, 2024, Phoenix, Arizona, preprint 24-017. Englewood, CO: Society for Mining, Metallurgy & Exploration, 2024 Feb; :1-6
Federal Fiscal Year:

2024
Peer Reviewed:

False
Source Full Name:

MineXchange: 2024 SME Annual Conference & Expo, February 25-28, 2024, Phoenix, Arizona, preprint 24-017
Collection(s):

National Institute for Occupational Safety and Health
Main Document Checksum:

urn:sha-512:0b1b0780e53d836184e1c654479e4f7b8e52cdc83d36d6dc59371a24439bb5983126355bf21b2d33108139f6ac65692ea9252515a4090d5644d5f5493c80eb27
Download URL:

https://stacks.cdc.gov/view/cdc/215670/cdc_215670_DS1.pdf
File Type:

[PDF - 968.71 KB ]

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