Injecting dynamic real-time data into a DDDAS for forest fire behavior prediction

Roque Rodríguez*, Ana Cortés, Tomás Margalef

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

26 Citations (Scopus)


This work presents a novel idea for forest fire prediction, based on Dynamic Data Driven Application Systems. We developed a system capable of assimilating data at execution time, and conduct simulation according to those measurements. We used a conventional simulator, and created a methodology capable of removing parameter uncertainty. To test this methodology, several experiments were performed based on southern California fires.

Original languageAmerican English
Pages (from-to)489-499
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Issue numberPART 2
Publication statusPublished - 2009


  • Dynamic Data Driven Application System
  • Evolutionary computing
  • Forest fire prediction
  • HPC
  • Parallel computing

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