Deterministic optimization techniques to calibrate parameters in a wildland fire propagation model
To fight against forest fires, simple and improved models are more searched out due to the fact they are more easily understandable by the users. This actual model is part of the fire propagation models within a network. It is simple and easy to implement. However, it depends on several parameters t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Académie des sciences
2020-12-01
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| Series: | Comptes Rendus. Mécanique |
| Subjects: | |
| Online Access: | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.58/ |
| Summary: | To fight against forest fires, simple and improved models are more searched out due to the fact they are more easily understandable by the users. This actual model is part of the fire propagation models within a network. It is simple and easy to implement. However, it depends on several parameters that are difficult to measure or estimate precisely beforehand. The prediction by this model is therefore insufficient. A deterministic optimization method is introduced to calibrate its parameters. The optimized model was tested on several laboratory experiments and on two large-scale experimental fires. The comparison of the model results with those of the experiment shows a very significant improvement in its prediction with the optimal parameters. |
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| ISSN: | 1873-7234 |