Improving wildland fire prediction on MPI clusters

B. Abdalhaq; G. Bianchini; A. Cortés; T. Margalef; E. Luque

Improving wildland fire prediction on MPI clusters

B. Abdalhaq, G. Bianchini, A. Cortés, T. Margalef, E. Luque

Research output: Contribution to journal › Article › Research › peer-review

Abstract

One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. In this paper, a parallel optimization framework for improving wildland fire prediction is applied to a real laboratory fire. The proposed prediction methodology has been tested on a Linux cluster using MPI. © Springer-Verlag Berlin Heidelberg 2003.

Original language	English
Pages (from-to)	520-528
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	2840
Publication status	Published - 1 Dec 2003

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title = "Improving wildland fire prediction on MPI clusters",

abstract = "One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. In this paper, a parallel optimization framework for improving wildland fire prediction is applied to a real laboratory fire. The proposed prediction methodology has been tested on a Linux cluster using MPI. {\textcopyright} Springer-Verlag Berlin Heidelberg 2003.",

author = "B. Abdalhaq and G. Bianchini and A. Cort{\'e}s and T. Margalef and E. Luque",

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Improving wildland fire prediction on MPI clusters. / Abdalhaq, B.; Bianchini, G.; Cortés, A.; Margalef, T.; Luque, E.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 2840, 01.12.2003, p. 520-528.

Research output: Contribution to journal › Article › Research › peer-review

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AU - Abdalhaq, B.

AU - Bianchini, G.

AU - Cortés, A.

AU - Margalef, T.

AU - Luque, E.

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AB - One of the challenges still open to wildland fire simulators is the capacity of working under real-time constrains with the aim of providing fire spread predictions that could be useful in fire mitigation interventions. In this paper, a parallel optimization framework for improving wildland fire prediction is applied to a real laboratory fire. The proposed prediction methodology has been tested on a Linux cluster using MPI. © Springer-Verlag Berlin Heidelberg 2003.

M3 - Article

VL - 2840

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