Using Machine Learning and Regression Analysis to Classify and Predict Danger Levels in Burning Sites
Bibliographic record
Abstract
Firefighters go into burning structures to rescue trapped victims and put out the fire as soon as possible. Factors such as extreme temperatures, smoke, toxic gases, explosions, and falling objects inhibit their efficiency and risk their safety. These factors could change within a twinkle of an eye. Firefighters must be provided with accurate information and data about the burning site. They can make informed decisions about their duties and know when it is safe to enter and evacuate to reduce casualties. This research work presents Machine Learning (ML) and regression models for predicting the danger levels in burning sites and utilizes autonomous embedded system vehicles (AESV) to validate the models' performance to increase firefighters' safety. We investigated the classification performance of three ML methods: Support Vector Machines (SVM), Logistic Regression (LR), and k- Nearest Neighbors (k-NN) on the Cross Laminated Timber (CLT) data collected by the National Institute of Standards and Technology (NIST) and the National Research Council Canada while testing the impacts of laminated timber in a controlled fire temperature. We have reported promising results for danger levels classification with the three models, but the k-NN performed slightly better than the other two classifiers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".