Improved Prediction Methods for Wildfires Using High Performance Computing: A Comparison

  • Germán Bianchini
  • Ana Cortés
  • Tomàs Margalef
  • Emilio Luque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


Recently, dry and hot seasons have seriously increased the risk of forest fire in the Mediterranean area. Wildland simulators, used to predict fire behavior, can give erroneous forecasts due to lack of precision for certain dynamic input parameters. Developing methods to avoid such parameter problems can improve significantly the fire behavior prediction. In this paper, two methods are evaluated, involving statistical and uncertainty schemes. In each one, the number of simulations that must be carried out is enormous and it is necessary to apply high-performance computing techniques to make the methodology feasible. These techniques have been implemented in parallel schemes and tested in Linux cluster using MPI.


Wildland Fire Ignition Probability Fire Behavior Prediction Real Spread Forest Fire Spread 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Andrews, P.L.: BEHAVE: Fire Behavior prediction and modeling systems - Burn subsystem, part 1. General Technical Report INT-194. Odgen, UT, US Department of Agriculture, Forest Service, Intermountain Research Station (1986)Google Scholar
  2. 2.
    Beven, K., Binley, A.: The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 6, 279–298 (1992)CrossRefGoogle Scholar
  3. 3.
    Bianchini, G., Cortés, A., Margalef, T., Luque, E.: S 2 F 2 M - Statistical System for Forest Fire Management. In: Sunderam, V.S., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2005. LNCS, vol. 3514, pp. 427–434. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Abdalhaq, B., Bianchini, G., Cortés, A., Margalef, T., Luque, E.: Improving Wildland Fire Prediction on MPI Clusters. In: Dongarra, J., Laforenza, D., Orlando, S. (eds.) EuroPVM/MPI 2003. LNCS, vol. 2840, pp. 520–528. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Bevins, C.D.: FireLib User Manual & Technical Reference (1996),
  6. 6.
    ADAI - CEIF (Center of Forest Fire Studies),
  7. 7.
    Morgan, P., Hardy, C., Swetnam, T.W., Rollins, M.G., Long, D.G.: Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns. International Journal of Wildland Fire 10, 329–342 (2001)CrossRefGoogle Scholar
  8. 8.
    MPI: The Message Passing Interface Standard,
  9. 9.
    Project Spread, Forest Fire Spread Prevention and Mitigation,
  10. 10.
    Montgomery, D.C., Runger, G.C.: Probabilidad y Estadística aplicada a la Ingeniería. Limusa Wiley (2002) ISBN: 968-18-5914-6Google Scholar
  11. 11.
    Piñol, P., Salvador, R., Beven, K.: Model Calibration and uncertainty prediction of fire spread, pp. 99– 111 (2002) ISBN 90-77017-72-0Google Scholar
  12. 12.
    Rothermel, R.C.: A mathematical model for predecting fire spread in wildland fuels, USDA FS, Ogden TU, Res. Pap. INT-115 (1972)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Germán Bianchini
    • 1
  • Ana Cortés
    • 1
  • Tomàs Margalef
    • 1
  • Emilio Luque
    • 1
  1. 1.Departament d’Informàtica, E.T.S.EUniversitat Autònoma de BarcelonaBellaterra (Barcelona)Spain

Personalised recommendations