TY - JOUR
T1 - Enhancing multi-model forest fire spread prediction by exploiting multi-core parallelism
AU - Brun, Carlos
AU - Margalef, Tomàs
AU - Cortés, Ana
AU - Sikora, Anna
PY - 2014/1/1
Y1 - 2014/1/1
N2 - © 2014, Springer Science+Business Media New York. The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.
AB - © 2014, Springer Science+Business Media New York. The Two-Stage forest fire spread prediction methodology was developed to enhance forest fire evolution forecast by tackling the uncertainty of some environmental conditions. However, there are parameters, such as wind, that present a variation along terrain and time. In such cases, it is necessary to couple forest fire propagation models and complementary models, such as meteorological forecast and wind field models. This multi-model approach improves the accuracy of the predictions by introducing an overhead in the execution time. In this paper, different multi-model approaches are discussed and the results show that the propagation prediction is improved. Exploiting multi-core architectures of current processors, we can reduce the overhead introduced by complementary models.
KW - Efficiency
KW - Forest fire
KW - HPC
KW - Multi-core
KW - Multi-model
KW - Prediction
U2 - 10.1007/s11227-014-1168-z
DO - 10.1007/s11227-014-1168-z
M3 - Article
SN - 0920-8542
VL - 70
SP - 721
EP - 732
JO - Journal of Supercomputing
JF - Journal of Supercomputing
ER -