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A Bayesian Network Model for Fire Assessment and Prediction

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Machine Learning, Optimization, and Big Data (MOD 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9432))

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Abstract

Smartphones and other wearable computers with modern sensor technologies are becoming more advanced and widespread. This paper proposes exploiting those devices to help the firefighting operation. It introduces a Bayesian network model that infers the state of the fire and predicts its future development based on smartphone sensor data gathered within the fire area. The model provides a prediction accuracy of 84.79 % and an area under the curve of 0.83. This solution had also been tested in the context of a fire drill and proved to help firefighters assess the fire situation and speed up their work.

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Author information

Authors and Affiliations

  1. Centre for Integrated Emergency Management, University of Agder, Grimstad, Norway

    Mehdi Ben Lazreg, Jaziar Radianti & Ole-Christoffer Granmo

Authors
  1. Mehdi Ben Lazreg
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  2. Jaziar Radianti
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  3. Ole-Christoffer Granmo
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Corresponding author

Correspondence to Mehdi Ben Lazreg .

Editor information

Editors and Affiliations

  1. University of Florida, Gainsville, Florida, USA

    Panos Pardalos

  2. University of Catania, Catania, Italy

    Mario Pavone

  3. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  4. University of Catania, Catania, Italy

    Vincenzo Cutello

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© 2015 Springer International Publishing Switzerland

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Lazreg, M.B., Radianti, J., Granmo, OC. (2015). A Bayesian Network Model for Fire Assessment and Prediction. In: Pardalos, P., Pavone, M., Farinella, G., Cutello, V. (eds) Machine Learning, Optimization, and Big Data. MOD 2015. Lecture Notes in Computer Science(), vol 9432. Springer, Cham. https://doi.org/10.1007/978-3-319-27926-8_24

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  • DOI: https://doi.org/10.1007/978-3-319-27926-8_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27925-1

  • Online ISBN: 978-3-319-27926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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