Canada’s Wildfire Susceptibility Assessment Using Statistical Data-Driven Models
Bibliographic record
Abstract
AbstractWildfire Susceptibility Assessment (WSA) is one of the critical approaches to wildfire risk management. In this study, we employed a hybrid approach by integrating two distinct statistical models, namely Frequency Ratio (FR), Weight of Evidence (WoE), with Shannon Entropy (SE) (i.e., FR-SE and WoE-SE) for WSA. To meet the aim, 18538 historical wildfire data were collected and separated into two sections for model development and validation. Next, 13 wildfire-influencing parameters, including slope degree, aspect, topographic wetness index, elevation, evapotranspiration, land use/land cover, normalized differences vegetation index, distance from the lake, precipitation, distance from the rivers, distance from the roads, soil moisture, and mean annual maximum temperature were prepared and feed the models. Finally, model performance were evaluated using the validation data set and receiver operating characteristic (ROC) curve technique. Findings shows that the integration of models has improved the modeling performance, as WOE-SE model has the highest performance (96.5%), followed by WoE (96.3%), SE-RF (95.9%) and RF (95.2%) model respectively. Result of SE model showed that mean annual maximum temperature has the highest impact on the wildfire occurrence across Canada, while topographic wetness index is the lowest effective parameter.Keywords: Wildfire, statistical models, Canada, Shannon Entropy, Frequency ratio, Weight of Evidence.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".