Modeling noise annoyance caused by air traffic using fuzzy logic
Bibliographic record
Abstract
The main goal of this project is the study and modeling of the noise annoyance caused by air traffic by using the fuzzy logic theory. Like many other environmental problems, air traffic noise, continues to grow and has become a serious problem in many countries. Millions of people living or working around airport areas can suffer from noise exposure effects as for instance hearing loss, interference with communication, stress, sleep disturbance, psychological effects as well as a general reduction in quality of life and tranquillity. However, noise annoyance is a difficult issue to evaluate as it is open to subjective reactions. Fuzzy logic theory is the perfect tool to analyse and evaluate all that vague and imprecise concepts that contrary to many other concepts, like age, distance or time, can not be measured as easily. In this project, a fuzzy function has been developed to quantify the annoyance level that people living or working in areas near airports are suffering. The parameters that come into play in this feature are the noise level, time of day, the number of events per hour, and finally, if it is a residential or an industrial area. Finally, the results of the fuzzy system have been represented on graphics that show the levels of nuisance caused by aircraft noise in each of the situations according the variables of the fuzzy function.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".