Machine Learning Approaches Used for Air Quality Forecast: A Review
Bibliographic record
Abstract
Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".