Wildfire Occurrence Prediction Using Time Series Classification: A Comparative Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We compare the effectiveness of four machine learning models at predicting wildfire occurrence from multivariate time series containing hourly weather data, vegetation data, and fire occurrence data. Strategies to improve performance on highly imbalanced datasets are investigated, including adapting KNN and HMM to consider cost effectiveness. Two different training regimes are compared: the imbalanced training regime varies the class imbalance in the training and testing datasets together, and the balanced training regime keeps the imbalance ratio 50:50 for every training dataset. FCN and ResNet outperform KNN and HMM across all class imbalances tested. We tested the methods on the SaskFire dataset, which is an extensive, new dataset describing wildfires in Saskatchewan, Canada. The two models that performed best on highly imbalanced datasets are FCN and ResNet trained with the imbalanced training regime. On our dataset with a non-fire to fire class imbalance of 99:1, FCN and ResNet have precisions of 0.190 and 0.250, respectively, and recalls of 0.800.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 it