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 |
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Pages (from-to) | 275-282 |
Number of pages | 8 |
Journal | Proceedings - 2008 IEEE 11th International Conference on Computational Science and Engineering, CSE 2008 |
DOIs | |
Publication status | Published - 2008 |
Keywords
- Dynamic data driven application system
- Evolutionary computing
- Forest fire prediction
- High performance computing
- Parallel computing
- Parallel simulation