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The Scientific World Journal
Volume 2013 (2013), Article ID 728414, 12 pages
http://dx.doi.org/10.1155/2013/728414
Research Article

Applying Probability Theory for the Quality Assessment of a Wildfire Spread Prediction Framework Based on Genetic Algorithms

Computer Architecture and Operating Systems Department, Autonomous University of Barcelona, Bellaterra, 08193 Barcelona, Spain

Received 1 October 2013; Accepted 18 November 2013

Academic Editors: T. Chen, Q. Cheng, and J. Yang

Copyright © 2013 Andrés Cencerrado et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. European Environment Agency, “Mapping the impacts of natural hazards and technological accidents in Europe,” Tech. Rep. 13, 2010. View at Google Scholar
  2. R. Salvador, R. Díaz-Delgado, J. Valeriano, and X. Pons, “Remote sensing of forest fires,” in Proceedings of GIS PlaNET’98 International Conference and Exhibition on Geographic Information (CD-ROM), 1998.
  3. J. Yang and Z. Fei, “HDAR: hole detection and adaptive geographic routing for ad hoc networks,” in Proceedings of the19th International Conference on Computer Communications and Networks (ICCCN '10), August 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Huang, A. J. Plaza, and Z. Wu, “High-Performance Computing in Remote Sensing III,” in Proceedings of SPIE, vol. 8895, 2013.
  5. Y. S. Chang, D. Bao, G. X. Liu, W. Wu, and N. Ao, “The study of grassland fire loss assessment method based on remote sensing technology in inner Mongolia,” in Intelligent Systems and Decision Making for Risk Analysis and Crisis Response: Proceedings of the 4th International Conference on Risk Analysis and Crisis Response, Istanbul, Turkey, 27-29 August 2013, CRC Press, 2013. View at Google Scholar
  6. R. Rothermel, “A mathematical model for predicting fire spread in wildland fuels,” Tech. Rep. INT-115, USDA Forest Service, Ogden, Utah, USA, 1972. View at Google Scholar
  7. C. D. Bevins, “Firelib user manual and technical reference,” 2008, http://www.fire.org/downloads/fireLib/1.0.4/doc.html.
  8. A. M. G. Lopes, M. G. Cruz, and D. X. Viegas, “Firestation—an integrated software system for the numerical simulation of fire spread on complex topography,” Environmental Modelling and Software, vol. 17, no. 3, pp. 269–285, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. M. Finney, “FARSITE: Fire Area Simulator-model development and evaluation,” Tech. Rep. RMRS-RP-4, US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, USA, 1998. View at Google Scholar
  10. G. L. Achtemeier, “Field validation of a free-agent cellular automata model of fire spread with fire-atmosphere coupling,” International Journal of Wildland Fire, vol. 22, no. 2, pp. 148–156, 2012. View at Google Scholar
  11. E. Innocenti, X. Silvani, A. Muzy, and D. R. C. Hill, “A software framework for fine grain parallelization of cellular models with OpenMP: application to fire spread,” Environmental Modelling and Software, vol. 24, no. 7, pp. 819–831, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. F. A. Sousa, R. J. N. dos Reis, and J. C. F. Pereira, “Simulation of surface fire fronts using fireLib and GPUs,” Environmental Modelling & Software, vol. 38, pp. 167–177, 2012. View at Google Scholar
  13. B. Abdalhaq, A. Cortés, T. Margalef, and E. Luque, “Enhancing wildland fire prediction on cluster systems applying evolutionary optimization techniques,” Future Generation Computer Systems, vol. 21, no. 1, pp. 61–67, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Madsen and F. Jakobsen, “Cyclone induced storm surge and flood forecasting in the northern Bay of Bengal,” Coastal Engineering, vol. 51, no. 4, pp. 277–296, 2004. View at Publisher · View at Google Scholar · View at Scopus
  15. S. D. Aberson, “Five-day tropical cyclone track forecasts in the North Atlantic basin,” Weather and Forecasting, vol. 13, no. 4, pp. 1005–1015, 1998. View at Google Scholar · View at Scopus
  16. H. Weber, “Hurricane track prediction using a statistical ensemble of numerical models,” Monthly Weather Review, vol. 131, pp. 749–770, 2003. View at Google Scholar
  17. T. Weise, Global Optimization Algorithms: Theory and Application, Thomas Weise, 2nd edition, 2009.
  18. Y. Celik and E. Ulker, “An improved marriage in honey bees optimization algorithm for single objective unconstrained optimization,” The Scientific World Journal, vol. 2013, Article ID 370172, 11 pages, 2013. View at Publisher · View at Google Scholar
  19. S. Liu, H. Jin, X. Mao, B. Zhai, Y. Zhan, and X. Feng, “Selective segmentation for global optimization of depth estimation in complex scenes,” The Scientific World Journal, vol. 2013, Article ID 868674, 9 pages, 2013. View at Publisher · View at Google Scholar
  20. J. Qin, L. Ni, and F. Shi, “Combined simulated annealing algorithm for the discrete facility location problem,” The Scientific World Journal, vol. 2012, Article ID 576392, 7 pages, 2012. View at Publisher · View at Google Scholar
  21. K. Sheng Lim, Z. Ibrahim, S. Buyamin et al., “Improving vector evaluated particle swarm optimisation by incorporating nondominated solutions,” The Scientific World Journal, vol. 2013, Article ID 510763, 19 pages, 2013. View at Publisher · View at Google Scholar
  22. M. Denham, K. Wendt, G. Bianchini, A. Cortés, and T. Margalef, “Dynamic data-driven genetic algorithm for forest fire spread prediction,” Journal of Computational Science, vol. 3, no. 5, pp. 398–404, 2012. View at Google Scholar
  23. B. Abdalhaq, A. Cortés, T. Margalef, and E. Luque, “Optimization of parameters in forest fire propagation models,” Proceedings of the 4th International Conference on Forest Fire Research, Luso, Portugal, 2002.
  24. B. Abdalhaq, A methodology to enhance the prediction of forest fire propagation [Ph.D. thesis], Universitat Autònoma de Barcelona, 2004.
  25. G. Bianchini, M. Denham, A. Cortés, T. Margalef, and E. Luque, “Wildland fire growth prediction method based on multiple overlapping solution,” Journal of Computational Science, vol. 1, no. 4, pp. 229–237, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. T. M. Mitchell, Machine Learning, McGraw-Hill, New York, NY, USA, 1997.
  27. T. Artés, A. Cencerrado, A. Cortés, and T. Margalef, “Relieving the effects of uncertainty in forest fire spread prediciton by hybrid MPI-OpenMP parallel strategies,” Procedia Computer Science, vol. 18, pp. 2278–2287, 2013. View at Google Scholar
  28. A. Cencerrado, R. Rodriguez, A. Cortés, and T. Margalef, “Urgency versus accuracy: dynamic data driven application system for natural hazard management,” International Journal of Numerical Analysis and Modeling, vol. 9, no. 2, pp. 432–448, 2012. View at Google Scholar
  29. F. A. Albini, “Estimating wildfire behavior and effects,” Tech. Rep. INT-30, US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah, USA, 1976. View at Google Scholar
  30. J. Burriel, F. Castro, T. Mata, D. Montserrat, E. G. Francisco, and J. Ibañez J, “La mejora del mapa diario de riesgo de incendio forestal en Cataluña,” in El acceso a la información espacial y las nuevas tecnologías geográficas, pp. 651–666, 2006.