Optimization of the Canadian Fire Weather Index (FWI) for the Mediterranean region
Bibliographic record
Abstract
Fire weather prognosis tools are of great importance for mitigating the catastrophic impacts of wildfires posed on human lives, valuable resources and assets. Their importance is getting higher if we reflect on the side effects of climate change such as rising temperatures, extreme drought phenomena and shifting precipitation patterns. These factors contribute to the heightened frequency and severity of wildfires. The Canadian Fire Weather Index (FWI) System stands out as one of the most extensively used tools for fire weather prognosis. Its reliability has been demonstrated across various forest types worldwide. Nevertheless, the FWI equations were initially developed within Canadian boreal forests, which posses different characteristics compared to other forest types, like Mediterranean forests, in terms of their vegetation and climatic conditions. The goal of this project is to refine the already effective Canadian Fire Danger System (FWI) and tailor it for the different characteristics of the Mediterranean climate reference region in order to get improved fire weather prognosis for that particular geographical region. The first part of the study is finding constants in the equations of the FWI that result from empirical calculations or laboratory tests with region specific characteristics and altering them so as to get different FWI values, that give better or worse fire weather prognosis. Each alteration is rated as better or worse depending on its correlation yield between the corresponding FWI values and Burned Area. The second part of the study is correlating the variables mentioned above using two methods. The first method is the correlation of all the grid boxes of the study region with Burned Area data and the second is the correlation on each grid box by itself providing that enough data of Burned Area is available for it. Firstly, this study indicates that an underlying positive correlation exists between the average monthly FWI values and the logBA values, which confirms the reliability of the Canadian FWI System. Secondly, using the first method of correlation, the altered FWI codes showed an increase in correlation of up to 10%, suggesting that optimizating the FWI for the Mediterranean climate refernce region is feasible. However, it is noticed that despite accomplishing the goal of increased correlation, there is a noteable difference between the FWI values of the optimized and original FWI code. Special attention should be given on this observation, since certain FWI values are associated with certain fire risk thresholds for different regions. Moreover, using the second method of correlation, from the Figures of Correlation Map and Correlation Map Difference, no clear pattern of increase or decrease in correlation was observed, throughout the study region. This pattern could be cleared out, either by using a broader study period or by accounting for the land use and vegetation type of each gridbox.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".