Spatial Susceptibility Mapping of Boreal Forest Fires: Insights from Quebec’s Historical and Future Trends (1980-2050)
Bibliographic record
Abstract
Abstract. Forest fires cause significant loss and damage each year, with climate change exacerbating their frequency and severity, highlighting the need for accurate susceptibility maps for effective mitigation and planning. This study integrating various environmental, ecological, and meteorological factors assesses the current and future forest fire susceptibility of Quebec’s boreal forests under two climate change scenarios over the next 30 years (2021-2050). The study involved identifying factors affecting forest fires and collecting 40 years of historical forest fire data (1980-2020). Climate variables were downloaded, and the Fire Weather Index (FWI) was calculated using BioSIM software and then interpolated into raster layers in ArcGIS Pro. The data was divided into training (70%) and testing (30%) sets, with a Random Forest (RF) model trained and validated using three accuracy metrics including receiver operating characteristics-area under the curve (ROC-AUC), the figure of merit (FoM), and F1 score, achieving results of 0.895, 0.808, and 0.894, respectively. Although forest fire susceptibility maps displayed some variation over the next 30 years (2021-2050), no distinct upward or downward trend was detected. Additionally, susceptibility remained largely unchanged under both the RCP 4.5 and RCP 8.5 scenarios. The study also highlighted the key factors affecting fire susceptibility, with the FWI, live biomass, and dead biomass being the most significant, contributing 21.8%, 14.28%, and 11.35%, respectively. This study predicting fire susceptibility and providing current and future susceptibility maps offers a proactive approach to climate change preparedness and improving resource allocation and forest fire risk management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".