Application of the Canadian Fire Weather Index for Forest Fire Danger Assessment in South Korea
Bibliographic record
Abstract
Climate change has led to the global intensification of wildfire activity, and South Korea has also experienced a marked increase in fire danger. To understand these developments, it is essential to examine both temporal trends and spatial patterns in fire-conducive climate conditions. In this study, we investigated the temporal and spatial characteristics of wildfire danger across South Korea between 2004 and 2023 using the Fire Weather Index (FWI). The analysis of long-term climate trends revealed region-specific patterns of increasing temperatures, decreasing precipitation, and declining wind speeds, which collectively contribute to an increased risk of wildfires. The FWI exhibited strong seasonal variations, with significant increases observed in spring, particularly in May, over the most recent decade. Statistical analyses confirmed a strong correlation between high FWI percentiles and wildfire occurrence, particularly noting an increased frequency of large-scale fires (>100 ha) in the highest FWI bins. The spatial analysis further highlighted that certain provinces, including Gangwon State, Gyeongsangbuk-do, Chuncheongbuk-do, and Gyeonggi-do, experienced disproportionately high increases in FWI values. These findings suggest that the FWI can serve as a robust framework for wildfire danger assessment in South Korea, particularly when supported by region-specific calibration and long-term validation under varying climatic conditions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".