Titolo:  GPU-accelerated multi-objective optimization of fuel treatments for mitigating wildfire hazard
Data di pubblicazione:  2015
Autori: 
Autori:  Arca, B; Ghisu, Tiziano; Trunfio, G. A.
Numero degli autori:  3
Lingua:  Inglese
Presenza coautori internazionali:  no
Rivista:  JOURNAL OF COMPUTATIONAL SCIENCE
Pagina iniziale:  258
Pagina finale:  268
Numero di pagine:  11
Digital Object Identifier (DOI):  http://dx.doi.org/10.1016/j.jocs.2015.08.009
Codice identificativo Scopus:  2-s2.0-84983575317
Codice identificativo ISI:  WOS:000367774700027
Abstract:  Fueltreatmentis considered a suitable way to mitigate the hazard related to potential wildfires on a landscape. However, designing an optimal spatial layout oftreatment units represents a difficult optimization problem. In fact, budget constraints, probabilistic nature of fire behaviour and complex interactions among the different fuel treatment patches, give rise to challenging search spaces on typical landscapes. In this study, we formulate the design problem in terms of a bi-objective optimization: minimizing both the extension of land characterized by high fire hazard and the cost of treatment. Then, we propose a computational approach that leads to a Pareto approximation set by exploiting an adapted version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) together with General-Purpose computing on Graphics Processing Units (GPGPU). Using an application example based on a real landscape, we also show that the proposed methodology has the potential to effectively support the design of a suitable fuel treatment for a landscape.
Parole Chiave:  GPGPU, Cellular automata, Wildfire simulation, Hazard maps, Multiobjective optimization
Tipologia: 1.1 Articolo in rivista

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