Using cellular automata to assess the role played by wind direction in two large fire episodes in Portugal
Bibliographic record
Abstract
Portugal is recurrently affected by severe wildfires, the fire season of 2017 representing the most tragic year with half a million of hectares burned and 115 deaths. The events that took place on October 15, 2017 deserve special attention, not only because the area burned on that day represents more than 50% of that burned during the entire year, but also because it resulted from the combination of very strong winds steered by the passage of hurricane Ophelia, very dry vegetation because of a prolonged drought affecting the country, very low atmospheric relative humidity and a record number of ignitions. Meteorological fire danger is usually rated using the Fire Weather Index (FWI) that is part of the Canadian Forest Fire Weather Index System. However, wind direction is not taken into account when defining FWI, and therefore it is worth investigating how this factor may affect the evolution of a given fire keeping all the remaining factor unaltered. The role played by wind direction is assessed using a cellular automata (CA) model to simulate two wind-driven wildfires that took place at Pataias-Burinhosa and Quiais on October 15, 2017. The CA model is first calibrated using winds derived from a regional weather forecasting model and sensitivity studies are then performed by systematically rotating the forecasted winds keeping all the other parameters constant. Results indicate a a progressive decrease in probability of burning from a 45º to a 90º counterclockwise rotation. These results suggest improving FWI by defining an FWI vector definition of an FWI vector, whose direction is that of the wind and magnitude is that of FWI. This vector should then be compared against the prevailing orientation of the vegetated area, and the closer the alignment between the two directions, the greater the meteorological fire danger.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".