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dc.contributor.authorArtès, Tomás
dc.contributor.authorCardil Forradellas, Adrián
dc.contributor.authorCortés, Ana
dc.contributor.authorMargalef, Tomàs
dc.contributor.authorMolina Terrén, Domingo
dc.contributor.authorPelegrín, Lucas
dc.contributor.authorRamírez, Joaquín
dc.descriptionInternational Conference on Computational Science, ICCS 2015 – Computational Science at the Gates of Nature
dc.description.abstractForest fire devastate every year thousand of hectares of forest around the world. Fire behavior prediction is a useful tool to aid coordination and management of human and mitigation resources when fighting against these kind of hazards. Any fire spread forecast system requires to be fitted with different kind of data with a high degree of uncertainty, such as for example, me- teorological data and vegetation map among others. The dynamics of this kind of phenomena requires to develop a forecast system with the ability to adapt to changing conditions. In this work two different fire spread forecast systems based on the Dynamic Data Driven Application paradigm are applied and an alternative approach based on the combination of both predictions is presented. This new method uses the computational power provided by high performance computing systems to deliver the predictions under strict real time constraints.ca_ES
dc.description.sponsorshipThis research has been supported by the Ministerio de Economía y Competitividad (MECSpain) under contract TIN2011-28689-C02-01 and the Catalan government under grant 2014- SGR-576.ca_ES
dc.relation.isformatofReproducció del document publicat a
dc.relation.ispartofProcedia Computer Science, 2015, vol. 51, p. 1623-1632ca_ES
dc.rightscc-by-nc-nd (c) Artès et al., 2016ca_ES
dc.subjectDynamic data drivenca_ES
dc.subjectParallel computingca_ES
dc.subjectData uncertaintyca_ES
dc.titleForest fire propagation prediction based on overlapping DDDAS forecastsca_ES

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cc-by-nc-nd (c) Artès et al., 2016
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Artès et al., 2016