Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction

Applying a Dynamic Data Driven Genetic Algorithm to Improve Forest Fire Spread Prediction. Denham, M., Cortés, A., Margalef, T., & Luque, E. In Bubak, M., Albada, G., Dongarra, J., & Sloot, P., editors, Computational Science - ICCS 2008, volume 5103, of Lecture Notes in Computer Science, pages 36–45. Springer Berlin Heidelberg.

Paper doi abstract bibtex

This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.

@incollection{denhamApplyingDynamicData2008,
  title = {Applying a {{Dynamic Data Driven Genetic Algorithm}} to {{Improve Forest Fire Spread Prediction}}},
  booktitle = {Computational {{Science}} - {{ICCS}} 2008},
  author = {Denham, Mónica and Cortés, Ana and Margalef, Tomàs and Luque, Emilio},
  editor = {Bubak, Marian and Albada, GeertDick and Dongarra, Jack and Sloot, PeterM},
  date = {2008},
  volume = {5103},
  pages = {36--45},
  publisher = {{Springer Berlin Heidelberg}},
  doi = {10.1007/978-3-540-69389-5\\_6},
  url = {https://doi.org/10.1007/978-3-540-69389-5_6},
  abstract = {This work represents the first step toward a DDDAS for Wildland Fire Prediction where our main efforts are oriented to take advantage of the computing power provided by High Performance Computing systems to, on the one hand, propose computational data driven steering strategies to overcome input data uncertainty and, on the other hand, to reduce the execution time of the whole prediction process in order to be reliable during real-time crisis. In particular, this work is focused on the description of a Dynamic Data Driven Genetic Algorithm used as steering strategy to automatic adjust certain input data values of forest fire simulators taking into account the underlying propagation model and the real fire behavior.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-12031791,dddas,dynamic-data-driven-application-system,forest-fires,forest-resources,genetic-algorithms,wildfires},
  series = {Lecture {{Notes}} in {{Computer Science}}}
}

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