Bridging spatiotemporal wildfire prediction and decision modeling using transformer networks and fuzzy inference systems
Bibliographic record
Abstract
Wildfires present a growing threat to ecosystems, human settlements, and climate stability, necessitating accurate and interpreted prediction systems. Existing AI-based models often prioritize performance over explainability, limiting their utility in real-time decision-making contexts. Current wildfire forecasting models struggle to incorporate uncertainty and offer transparent response strategies. Moreover, many models fail to integrate domain knowledge in a way that supports actionable interventions. This study utilizes the Canadian Fire Spread Dataset, augmented with Sentinel, ERA5, and SRTM data, encompassing vegetation, meteorological, and topographic variables. The suggested system uses a Transformer-based model to predict fires over time and space, along with a Fuzzy Rule-Based System (FRBS) to create rules for responding to those predictions. This integration allows for both high accuracy and interpretability in decision-making under uncertain environmental conditions. The novelty lies in the use of symbolic fuzzy reasoning layered onto a deep attention-based architecture. Performance was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The model achieved an F1-score of 92.9 % and accuracy of 94.8 %, significantly outperforming baseline and deep learning alternatives. • Integrates deep learning with fuzzy logic for both accurate forecasting and interpretable response planning. • Enables uncertainty-aware reasoning by translating predictions into actionable fire management rules. • Demonstrates superior performance across diverse environmental datasets using multi-source satellite and climate inputs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".