Fire Danger Characterization in Italy: Mitigating the Impact on Real Time Operation of the Power System
Bibliographic record
Abstract
Wildfires are highly disruptive events for the continuity of the electricity system, as they can directly affect electrical infrastructures, or they need transmission overhead lines to be deactivated for allowing firefighters to deal with the flames. They are also events that are extremely hard to forecast, as they mainly need human actions (or negligence) for ignition. Fire weather indices compute wildfire danger from meteorological data, thus allowing stakeholders to take preventive measures where there is a high fire spread risk. In this paper, the Canadian Fire Weather Index (FWI) has been used to assess daily wildfire danger over the Italian territory for the past decades, using as input the high-resolution reanalysis MERIDA HRES OI, developed by RSE S.p.A. MERIDA HRES OI is a gridded meteorological dataset at hourly resolution covering Italy, with a 4km spatial resolution. It is a reconstruction of the past meteorological conditions using a numerical weather model and integrating experimental data through Optimal Interpolation. The FWI dataset obtained from MERIDA HRES has been tested against the EFFIS Burned Area Dataset to assess its accuracy in representing past fire-prone weather, in which wildfires ignited and spread. This analysis shows that the dataset correctly classifies more than 80% of the wildfire events in the “high” danger class or above. The study on the FWI reanalysis dataset also allowed for the development of an FWI-based wildfire danger forecasting system, currently being tested in an operational environment. A test case study is discussed to showcase the accuracy at different lead times of the FWI forecasting system prototype.
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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 | 0.000 |
| 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".