Simulation of Tehran Air Pollution Using Artificial Neural Networks
Bibliographic record
Abstract
Air pollution is one of the most important environmental issues of the populated cities, which is seriously threatening human health. This is caused by several factors, such as climo-physiologic characteristics of the region, deficiency of perennial plants, vehicles-mobile pollution sources- and also stationary sources including factories and power plants. Vehicles' gas emission is the major source of air pollution which is related to the type and magnitude of fuel they consume. In comparison, the stationary sources only contribute one fifth of the total pollutant emission. In this study, the effect of fuel consumption increase on Tehran (Iran's capital) air pollution is evaluated. Obviously, weather patterns have also considerable role on air pollution which are considered in the proposed simulation model. For this purpose, the most effective parameters on Tehran air pollution are identified and selected as the input of simulation model. Then, Artificial Neural Network (ANN) models are chosen due to their efficient power and robustness in identifying the nature of complicated phenomena which air pollution is one of them. Tehran is selected as a case study because of its higher violation to the air pollution standards and being one of the five polluted cities around the world. Results show that the proposed model can be implemented successfully to monitor the air pollution changes and consequently makes it possible for associated managers to develop appropriate policies against the air pollution.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".