Atmospheric Modeling for Wildfire Prediction
Bibliographic record
Abstract
Due to climate change, forest regions in California, Western Australia, and Saskatchewan, Canada, are increasingly experiencing severe wildfires, with other climate-related issues affecting the rest of the world. Machine learning (ML) and artificial intelligence (AI) models have emerged to predict wildfire hazards and aid mitigation efforts. However, inconsistencies arise in the wildfire prediction modeling domain due to the database adjustments required to enable complex and real-time modeling. To help address this issue, our paper focuses on creating wildfire prediction models through One-class classification algorithms: Support Vector Machine, Isolation Forest, AutoEncoder, Variational AutoEncoder, Deep Support Vector Data Description, and Adversarially Learned Anomaly Detection. Five-fold Cross-Validation was used to validate all One-class ML models to minimize bias in the selection of the training and testing data. These One-class ML models outperformed Two-class ML models using the same ground truth data, with mean accuracy levels between 90 and 99 percent. Shapley values were used to derive the most important features affecting the wildfire prediction model, which is a novel contribution to the field of wildfire prediction. Among the most important factors for models trained on the California data set were the seasonal maximum and mean dew point temperatures. These insights will support mitigation strategies. In providing access to our algorithms, using Python Flask and a web-based tool, the top-performing models were operationalized for deployment as a REST API, with outcomes supporting the potential of our solution for strengthening wildfire mitigation 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".