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Real Time Mine Fire Classification to Support Firefighter Decision Making

Fire Technology volume 58, pages 1545–1578 (2022)Cite this article

Abstract

This paper presents a data-driven approach that can provide the most suitable decision to the mine firefighting personnel in real time during ongoing underground coal mine fires. The approach uses a feed-forward artificial neural network (ANN) to classify fires to provide the best decision considering only parameters measurable in underground coal mines. Additionally, the methodology along with the concepts that should be considered to elaborate a data-driven approach of this type are detailed. A total of 500 fire scenarios with different fire size, air velocity, fire growth rate, and entry dimensions were simulated in Fire Dynamics Simulator (FDS) and Fire and Smoke Simulator (FSSIM) for data generation to train and test the model. Results show that the ANN predicted fire classes with an accuracy and weighted-average F1-score equals to 97% and 96.7% for training and testing dataset, respectively. Results also show that 95% of ANN predictions of fire class change should not have a time gap greater than 18 s of the true fire class change for any fire position in the tunnel. Furthermore, the impact of fuel uncertainty during mine fires and how to address it is discussed in this paper. While the model presented in this work was designed to classify fires in a regular elongated coal mine entry, the same methodology could be applied to classify fires in other scenarios with similar geometry, such as road tunnels.

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Availability of data and material

All datasets and codes used for supporting the conclusions of this article are available upon request at the following website: https://drive.google.com/drive/folders/1wRFvfTG_pTk0GRNUZWRfOGRDrcDSLWNA?usp=sharing.

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Acknowledgements

This research was supported by Contract No. 200-2014-59669, awarded by the National Institute for Occupational Safety and Health (NIOSH). The findings and conclusions in this report are those of the authors and do not reflect the official policies of the Department of Health and Human Services; nor does mention of trade names, commercial practices, or organizations imply endorsement by the U.S. Government.

Funding

This research was funded by the National Institute Safety and Health (NIOSH) under contract No. 200-2014-59669.

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Authors and Affiliations

  1. Jensen Hughes, 2020 Kraft Drive, Suite 3020, VA, 24060, Blacksburg, USA

    Manuel J. Barros-Daza & Jonathan L. Hodges

  2. Department of Mining and Minerals Engineering, Virginia Tech, 118A Surge Building, 400 Stanger Street (MC 0239), Blacksburg, VA, 24061, USA

    Kray D. Luxbacher

  3. Department of Mechanical Engineering, Virginia Tech, Goodwin Hall, Room 413D, Blacksburg, 24061, VA, USA

    Brian Y. Lattimer

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  1. Manuel J. Barros-Daza
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Correspondence to Manuel J. Barros-Daza.

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Barros-Daza, M.J., Luxbacher, K.D., Lattimer, B.Y. et al. Real Time Mine Fire Classification to Support Firefighter Decision Making. Fire Technol 58, 1545–1578 (2022). https://doi.org/10.1007/s10694-022-01215-4

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Keywords

  • Fire emergency response
  • ANN neural network
  • Mine fire classification
  • Mine firefighters