Cloud-based urgent computing for forest fire spread prediction
Fraga, Edigley (Universitat Autònoma de Barcelona)
Cortés Fité, Ana (Universitat Autònoma de Barcelona)
Margalef, Tomàs (Universitat Autònoma de Barcelona)
Hernández Budé, Porfidio (Universitat Autònoma de Barcelona)
Carrillo Jordan, Carlos (MITIGA SOLUTIONS S.L.)

Data:	2024
Resum:	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).
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Data uncertainty ; Forest fires ; Genetic algorithm ; Data-driven calibration ; Urgent computing ; Cloud computing
Publicat a:	Environmental modelling & software, Vol. 177 (June 2024) , art. 106057, ISSN 1873-6726

DOI: 10.1016/j.envsoft.2024.106057

14 p, 3.9 MB

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