Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada
Bibliographic record
Abstract
Abstract Accurate prediction of fire spread is considered crucial for facilitating effective fire management, enabling proactive planning, and efficient allocation of resources. This study places its focus on wildfires in two regions of Alberta, Fort McMurray and Slave Lake, in Southwest Canada. For the simulation of wildfire spread, an adapted fire propagation model was employed, incorporating MODIS datasets such as land surface temperature, land cover, land use, and integrated climate data. The pixels were classified as burned or unburned in relation to the 2011 Slave Lake wildfire and the initial 16 days of the 2016 Fort McMurray wildfire, utilizing defined starting points and the aforementioned specified datasets. The simulation for the 2011 Slave Lake wildfire achieved an weighted average precision, recall, and f1-scores of 0.989, 0.986, and 0.987, respectively. Additionally, macro-averaged scores across these three phases were 0.735, 0.829, and 0.774 for precision, recall, and F1-scores, respectively. The simulation of the 2016 Fort McMurray wildfire introduced a phased analysis, dividing the initial 16 days into three distinct periods. This approach led to average precision, recall, and f1-scores of 0.958, 0.933, and 0.942 across these phases. Additionally, macro-averaged scores across these three phases were 0.681, 0.772, and 0.710 for precision, recall, and F1-scores, respectively. The strategy of segmenting simulations into phases may enhance adaptability to dynamic factors like weather conditions and firefighting strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".