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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Rodríguez, R. Cortés, A. Margalef, T. |
| Copyright Year | 2010 |
| Abstract | In our research work, we use two Dynamic Data Driven Application System (DDDAS) methodologies to predict wildfire 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. In this paper, we propose a strategy for data injection in distributed environments. |
| Starting Page | 565 |
| Ending Page | 568 |
| File Size | 583424 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424469871 |
| e-ISBN | 9781424469888 |
| DOI | 10.1109/CCGRID.2010.74 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-05-17 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Fires Computational modeling Predictive models Application software Real time systems High performance computing Grid computing Hazards Concurrent computing Mathematical model Real-time data injection Dynamic Data Driven Application System Parallel computing Forest fire prediction Parallel simulation Evolutionary computing |
| Content Type | Text |
| Resource Type | Article |
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