Use and performance of the Forest Fire Weather Index to model the risk of wildfire occurrence in the Alpine region
Bibliographic record
Abstract
Assessing a territory’s fire proneness is fundamental when planning and undertaking effective forest protection and land management. Accurate methods to estimate the risk of fire ignition in natural environments have been proposed over the last decades and digital mapping has been used to identify critical areas. The Canadian Forest Fire Weather Index is a well-known fire danger rating index created and improved during the last 45 years by the Canadian Forest Service. The goal of this paper is twofold. Firstly, we evaluated whether the Forest Fire Weather Index is an adequate instrument to predict fire ignition in Alpine and sub-Alpine areas using quite a large dataset of meteorological and forest fire data collected in the Lombardy region (Northern Italy) between 2003 and 2011. By means of a spatial binary regression model, we demonstrated that Forest Fire Weather Index has a significant impact on the probability of fire ignition. Since this approach allows us to account for other characteristics of the territory in order to provide a more accurate estimate of the spatial wildfire dynamics at a moderately large scale, the second goal of the paper aims at creating a model to assess fire risk occurrence using the Forest Fire Weather Index and land use information. It has been found that ignition can easily occur in large forested areas whereas denser urban areas are less exposed to fire since they usually have no fuels to ignite. Nevertheless, since human activity has a direct impact on fire ignition human presence, it fosters ignition in forested areas. Finally, the model, including these spatial dimensions, has been employed to derive a probability map of fire occurrences at 1.5 km resolution, which is a fundamental instrument to develop optimal prevention and risk management policy plans for the decision maker.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".