Fuzzy Logic Modelling Of Air Pollution During Chinook Winds
Bibliographic record
Abstract
The occurrence of Foehn winds in the city of Calgary is due to the nearby proximity of the Canadian Rocky Mountains. The Foehn winds bring warm weather during the winter months and are described locally as Chinooks, a Blackfoot Indian term meaning snow eaters. Despite the increased wind speed during a Chinook, air pollution levels increase. A novel approach to modeling air pollution during a Chinook was developed using fuzzy logic. The Fuzzy c-Means method for system classification was used to identify the Foehn wind. As well, Modified Learning from Examples was used to predict surface ozone concentration. The identification of the occurrence of the Chinook wind was based on changes in humidity, and temperature increase. Key factors in air pollution for Calgary are ventiIation components such as wind speed. The new hzzy logic model developed in this work predicts the ozone concentration based on hourly wind speed and wind direction. The new model shows good agreement with the data and captures the trends in ozone concentration during Foehn wind conditions. Furthermore, this work shows that hzzy logic can be a powerful tool in air pollution modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".