Modeling of hourly NO<sub><i>x</i></sub> concentrations using artificial neural networks
Bibliographic record
Abstract
Modeling of ambient air quality is an important component of urban air quality management. Artificial neural network (ANN) modeling may offer advantages in understanding processes that follow nonlinear, complex relationships. Artificial neural network modeling is a black-box method where relationships describing complex situations are not necessarily known. The ANN models learn patterns based on historical data, and then conduct simulations based upon these patterns. The objective of this study was to evaluate the feasibility of using an ANN to predict ambient hourly concentrations of oxides of nitrogen (NOx) in an industrial corridor adjacent to Edmonton, Alberta. A standard 4-layer back-propagation network was used to predict ambient hourly NOx concentrations using industry stack emission rates, meteorological data, and traffic counts as input variables. The resulting model fit (R2 of 0.63) and precision of model prediction (root mean square error of 1.8 × 103 ppm as NO2) suggested that ANN modeling shows promise for predicting NOx behaviour; however, further work is necessary to improve its forecasting ability.Key words: urban air quality, airshed, modeling, artificial neural network.
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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 | 0.000 |
| 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".