Cloud-based urgent computing for forest fire spread prediction

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

*Autor correspondiente de este trabajo

Producción científica: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

1 Cita (Scopus)
1 Descargas (Pure)

Resumen

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).

Idioma originalInglés
Número de artículo106057
Páginas (desde-hasta)1-14
Número de páginas14
PublicaciónEnvironmental Modelling & Software
Volumen177
Fecha en línea anticipada27 abr 2024
DOI
EstadoPublicada - 1 jun 2024

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