Cloud-based urgent computing for forest fire spread prediction

Edigley Fraga*, Ana Cortes Fite, T. Margalef, Porfidio Hernandez Bude, Carlos Carrillo Jordan

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)
1 Downloads (Pure)

Abstract

Forest fires cause every year damages to biodiversity, atmosphere, and economy activities. Forest fire simulation have improved significantly, but input data describing fire scenarios are subject to high levels of uncertainty. In this work the two-stage prediction scheme is used to adjust unknown parameters. This scheme relies on an input data calibration phase, which is carried over following a genetic algorithm strategy. The calibrated inputs are then pipelined into the actual prediction phase. This two-stage prediction scheme is leveraged by the cloud computing paradigm, which enables high level of parallelism on demand, elasticity, scalability and low-cost. In this paper, all the models designed to properly allocate cloud resources to the two-stage scheme in a performance-efficient and cost-effective way are described. This Cloud-based Urgent Computing (CuCo) architecture has been tested using, as study case, an extreme wildland fire that took place in California in 2018 (Camp Fire).

Original languageEnglish
Article number106057
Pages (from-to)1-14
Number of pages14
JournalEnvironmental Modelling & Software
Volume177
Early online date27 Apr 2024
DOIs
Publication statusPublished - 1 Jun 2024

Keywords

  • Data uncertainty
  • Forest fires
  • Genetic algorithm
  • Data-driven calibration
  • Urgent computing
  • Cloud computing

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