Enhancing Multi-Model Forest Fire Spread Prediction by Exploiting Multi-Core Parallelism. Brun, C.; Margalef, T.; Cortés, A.; and Sikora, A. 70(2):721–732.
Enhancing Multi-Model Forest Fire Spread Prediction by Exploiting Multi-Core Parallelism [link]Paper  doi  abstract   bibtex   
The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.
@article{brunEnhancingMultimodelForest2014,
  title = {Enhancing Multi-Model Forest Fire Spread Prediction by Exploiting Multi-Core Parallelism},
  author = {Brun, Carlos and Margalef, Tomàs and Cortés, Ana and Sikora, Anna},
  date = {2014-04},
  journaltitle = {The Journal of Supercomputing},
  volume = {70},
  pages = {721--732},
  issn = {1573-0484},
  doi = {10.1007/s11227-014-1168-z},
  url = {https://doi.org/10.1007/s11227-014-1168-z},
  abstract = {The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13389690,forest-fires,forest-resources,parallelism},
  number = {2}
}
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