ML-Based Wildfire Prediction and Detection
Bibliographic record
Abstract
This paper provides a comprehensive study on prediction and detection of wildfire using Machine Learning and Deep Learning algorithms. Due to the current environmental trends, wildfire possess a great threat to the ecosystem and human lives at a great cost. Multiple factors are the root cause for wildfires which include environmental factors like temperature, humidity, air pressure index, forest terranean, vegetation. Taking these factors into consideration, a Machine Learning model was built considering diverse algorithms to learn the previous trends and predict future wildfires instances. Based on the satellite imagery of previous wildfires, using CNN and AlexNet algorithms to detect wildfires that are currently taking place for early detection so to contain and control the fire without it causing any damage. Amalgamating these two algorithms, in a single graphical user interface, enhances user accessibility and convenience, providing an invaluable tool in wildfire management. The algorithm achieved an accuracy of average 96.33 % to predict wildfires and was able to detect them based on images at the rate of 93.66%.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".