Fire weather indices tailored to regional patterns outperform global models
Bibliographic record
Abstract
Fire weather indices (FWIs) are widely used to assess wildfire risk, but are typically designed for specific regions and not adapted globally. Here, we present a systematic effort to generate country-specific FWIs that capture regional fire-weather patterns. We evaluate three widely used indices across countries, finding that the Canadian FWI performs best overall (ROC AUC of 0.69). Tailoring the index to each country with a Genetic Algorithm significantly improves its accuracy, raising the ROC AUC from 0.69 to 0.79. To further improve accuracy while maintaining interpretability, we develop a single Decision Tree model per country, achieving an ROC AUC of 0.86. Attempts to develop a single global Decision Tree yielded substantially lower accuracy, highlighting the limitations of universal models and the importance of capturing regional characteristics such as weather patterns, vegetation types, and topography for accurately predicting wildfire risk. Adapting FWIs regionally is crucial under accelerating climate change conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".