An adaptive system for forest fire behavior prediction

Roque Rodriguez*, Ana Cortés, Tomás Margalef, Emilio Luque

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)


In this paper, we propose a combination of two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfires' propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time.

Original languageEnglish
Pages (from-to)275-282
Number of pages8
JournalProceedings - 2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008
Publication statusPublished - 2008


  • Dynamic data driven application system
  • Evolutionary computing
  • Forest fire prediction
  • High performance computing
  • Parallel computing
  • Parallel simulation


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