An adaptive system for forest fire behavior prediction

Roque Rodriguez; Ana Cortés; Tomás Margalef; Emilio Luque

doi:10.1109/CSE.2008.15

An adaptive system for forest fire behavior prediction

Roque Rodriguez^*, Ana Cortés, Tomás Margalef, Emilio Luque

^*Corresponding author for this work

Department of Computer Architecture and Operating Systems

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

13 Citations (Scopus)

Abstract

In this paper, we propose a combination of two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfires' propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time.

Original language	English
Pages (from-to)	275-282
Number of pages	8
Journal	Proceedings - 2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008
DOIs	https://doi.org/10.1109/CSE.2008.15
Publication status	Published - 2008

Keywords

Dynamic data driven application system
Evolutionary computing
Forest fire prediction
High performance computing
Parallel computing
Parallel simulation

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10.1109/CSE.2008.15

Cite this

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abstract = "In this paper, we propose a combination of two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfires' propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time.",

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AU - Cortés, Ana

AU - Margalef, Tomás

AU - Luque, Emilio

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AB - In this paper, we propose a combination of two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfires' propagation. Our goal is to build a system that dynamically adapts to constant changes in environmental conditions when a hazard occurs and under strict real-time deadlines. For this purpose, we are on the way of building a parallel wildfire prediction method, which is able to assimilate real-time data to be injected in the prediction process at execution time.

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KW - Evolutionary computing

KW - Forest fire prediction

KW - High performance computing

KW - Parallel computing

KW - Parallel simulation

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ER -

An adaptive system for forest fire behavior prediction

Abstract

Keywords

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