Igniting Precision: Amplifying Wildfire Prediction in Diverse Regions via Teacher-Student Model Fusion
Bibliographic record
Abstract
Accurate wildfire prediction in diverse and geographically dispersed areas is crucial for effective wildfire management. However, the limited availability of labeled data in data-challenged regions, along with the unique characteristics of these areas, poses challenges for training robust prediction models. This study investigates the performance of a convolutional neural network (CNN) on datasets comprising Landsat images from Canada and Alaska. Through principal component analysis (PCA), the study uncovers distinct differences in data distribution between the two regions. It is observed that the reduced data size of the Alaskan dataset, along with its distinct data distribution, leads to a decrease in the CNN's accuracy to 75% compared to an impressive 98% achieved on the Canadian dataset. To address this limitation, we propose a teacher-student model approach, transferring knowledge from a CNN trained on the larger Canadian dataset. The results demonstrate a significant accuracy improvement to 88.96% on the Alaskan dataset. Our findings highlight the effectiveness of the teacherstudent model in mitigating data scarcity challenges, enhancing wildfire prediction capabilities in regions with limited training data. This research contributes to improved wildfire monitoring and prevention strategies in challenging geographical locations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".