SPARK – A Bushfire Spread Prediction Tool

  • Claire Miller
  • James Hilton
  • Andrew Sullivan
  • Mahesh Prakash
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)


Bushfires are complex processes, making it difficult to accurately predict their rate of spread. We present an integrated software system for bushfire spread prediction, SPARK, which was developed with the functionality to model some of these complexities. SPARK uses a level set method governed by a user-defined algebraic spread rate to model fire propagation. The model is run within a modular workflow-based software environment. Implementation of SPARK is demonstrated for two cases: a small-scale experimental fire and a complex bushfire scenario. In the second case, the complexity of environmental non-homogeneity is explored through the inclusion of local variation in fuel and wind. Simulations over multiple runs with this fuel and wind variation are aggregated to produce a probability map of fire spread over a given time period. The model output has potential to be used operationally for real-time fire spread modeling, or by decision makers predicting risk from bushfire events.


level set bushfire wildfire modeling simulation 


  1. 1.
    Tolhurst, K., Shields, B., Chong, D.: Phoenix: development and application of a bushfire risk management tool. The Australian Journal of Emergency Management 23, 47–54 (2008)Google Scholar
  2. 2.
    Johnston, P., Kelso, J., Milne, G.J.: Efficient simulation of wildfire spread on an irregular grid. International Journal of Wildland Fire 17, 614–627 (2008)CrossRefGoogle Scholar
  3. 3.
    Osher, S., Sethian, J.: Fronts Propagating with Curvature-Dependent Speed: Algorithms Based on Hamilton-Jacobi Formulations. Journal of Computational Physics 79(1), 12–49 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Sethian, J.A., Strain, J.: Crystal growth and dendritic solidification. Journal of Computational Physics 98(2), 231–253 (1992)CrossRefzbMATHMathSciNetGoogle Scholar
  5. 5.
    Sussman, M., Smereka, P., Osher, S.: A level set approach for computing solutions to incompressible two-phase flow. Journal of Computational Physics 114, 146–159 (1994)CrossRefzbMATHGoogle Scholar
  6. 6.
    Hilton, J., Miller, C., Sullivan, A., Rucinski, C.: Incorporation of variation into wildfire spread models using a level set approach. Submitted to Environmental Modelling and Software (under review)Google Scholar
  7. 7.
    OpenCL (2014), (accessed December 9, 2014)
  8. 8.
    Workspace (2014), (accessed October 31, 2014)
  9. 9.
    Sullivan, A.L., Gould, J.S., Cruz, M.G., Rucinski, C., Prakash, M.: Amicus: a national fire behaviour knowledge base for enhanced information management and better decision making. In: Piantadosi, J., Anderssen, R.S. (eds.) MODSIM 2013, 20th International Congress on Modelling and Simulation, pp. 2068–2074 (2013)Google Scholar
  10. 10.
    Cruz, M.G., Gould, J., Kidnie, S., Nichols, D., Anderson, W.R., Bessel, R., Hurley, R., Koul, V.: Grass curing and fire behavior. Submitted to International Journal of Wildland Fire (under review)Google Scholar
  11. 11.
    Mackie, B., McLennan, J., Wright, L.: Community Understanding and Awareness of Bushfire Safety: January 2013 Bushfires. Research for the New South Wales Rural Fire Service. Bushfire CRC. La Trobe University (2013)Google Scholar
  12. 12.
    Cheney, N.P., Gould, J.S., McCaw, W.L., Anderson, W.R.: Predicting fire behaviour in dry eucalypt forest in southern Australia. Forest Ecology and Management 280, 120–131 (2012)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Claire Miller
    • 1
  • James Hilton
    • 1
  • Andrew Sullivan
    • 2
  • Mahesh Prakash
    • 1
  1. 1.CSIRO Digital Productivity FlagshipMelbourneAustralia
  2. 2.CSIRO Land and Water FlagshipCanberraAustralia

Personalised recommendations