Dynamic simulation of fire propagation in forests and rangelands using a GIS-based cellular automata model
Bibliographic record
Abstract
Prediction of the way wildfires propagate in forests and rangelands is one of the critical issues in environmental protection and disaster management. This research aims to simulate wildfire propagation using a geographical information system (GIS)-based cellular automata (CA) model. The model considers the most effective spatial and temporal drivers of wildfire propagation, including wind speed and direction, type and density of vegetation, and topographic conditions. Wind speed and direction were considered changeable over the simulation process. A genetic algorithm (GA) was used to calibrate the model developed. Validation of the model was performed using an independent fire case assessed by the overall accuracy (OA) criterion and the Kappa coefficient index. The mean values obtained for the OA and the Kappa coefficient in 100 runs for two wildfire cases indicated that the proposed model could be effectively used in the simulation of wildfire propagation. The results of this model can assist fire managers in predicting fire propagation to better control wildfires.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".