PM10 CONCENTRATION PREDICTION FOR AREAS WITH NO UPDATING MONITORING SYSTEM USING AUTO – REGRESSIVEGROUP METHOD OF DATA HANDLING NEURAL NETWORK
Bibliographic record
Abstract
Introduction: Predicting PM10 concentration as a significant risk factor for anumber of pollution related diseases has been recently inevitable task for areas with high population density particularly for areas with no updating monitoring systems. This study aims to illustrate how PM10 concentration level can be predicted by the prior information of the air pollutants and the meteorologicalfactors in urban areas. Materials and methods: The data we used are measured from four monitoringstations in the city of Tehran between January 2012 and December 2014. We use the Auto-regressive group method of data handling (AR - GMDH) neuralnetwork approach which employees the prior stationary time series data setting. Results: Our results demonstrate that PM10 concentration level for a specific dayis more likely to be predictable by sulfur dioxide (SO2) and nitrogen dioxide (NO2) than the carbon monoxide (CO) concentrations, and also show thatPM10 concentration is positively associated with precipitation and wind speedand with high temperature. The accuracy of the predicted values of the PM10 concentration is evaluated by inspecting the coefficient of determination, meansquared error, the square root of mean squared error, mean absolute deviation, and index of agreement. Conclusions: The AR - GMDH algorithm can be proposedin comparison with the chemical and physical approaches due to its accuracyand simplicity, and its cost efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".