Developing a Mathematical Model to Assess the Liveablity in Blighted Mega City
Bibliographic record
Abstract
Karachi (24° 37.38¢ N, 66° 54.42¢ E) is one of the mega cities of Pakistan. In general, deteriorating urban air quality in developing countries is a worsening environmental problem and causing damage to human health. The urban atmospheric pollution is several times higher than the limits set by the WHO. Being industrial city, the rate of increase of traffic volume has been exponential during the last decade in Karachi. More or less, 90% atmospheric pollution is related to vehicle emissions. To gauge and forecast future traffic volume and urban pollution level mathematical models are needed. This communication attempts to model the urban traffic population evolution and the atmospheric pollution levels during the past 25 years. The traffic model shows that total traffic volume in Karachi was 1 million in 1999. In 2008 it reached 2 million and in 2012 the traffic volume crossed 3 million verifying published data. According to this forecast model, it is also important to note that the total traffic volume in Karachi will go to 4 million, 5 million, and 6 million in the years 2015, 2018, and 2020 respectively. Auto Regressive Integrated Moving Average, ARIMA (2, 1, 2) model is found to be adequate model to capture Karachi urban atmospheric pollution variation. The model is the first of its kind for the region considered. As a further application, we develop an empirical model of local atmospheric pollution fluctuations as determined by urban traffic volume. The work should provide a basis for other applications, including urban planning,urban-regional air quality management, design of efficient energy programs, etc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".