Variability of Noise Exposure Forecast Outputs Due to the Selection of Input Parameters
Bibliographic record
Abstract
The growth of civil aviation and the resulting aircraft noise exposure and noise annoyance have become a serious environmental issue for residents living around airports. Land use planning and management is one method to mitigate noise and annoyance. It is one of four components of the Balanced Approach to Aircraft Noise Management adopted by International Civil Aviation Organization (ICAO). Effective land use and planning tries to remove conflicts between noise and residential communities by keeping noise sensitive functions away from levels of predicted high noise exposure. Authorities typically use noise contours to determine noise exposure around airports. Noise thresholds are applied using said contours to identify the areas unsuitable for noise sensitive developments due to excessive noise. In Canada, Transport Canada advocates the Noise Exposure Forecast (NEF) noise contours as a tool that depicts long term noise exposure. The NEF contours are used to inform land use planning and management around the nation’s airports. These contours not only inform appropriate zoning but are also used to direct noise mitigation initiatives. Therefore, NEF contours are essential tools for aircraft noise and annoyance management and as such their accuracy and consistency are of critical importance. While advancements in modelling software have enhanced the accuracy of noise contours, one aspect of the modelling process, specifically the selection of input parameters, is yet to be standardized. Transport Canada mandates that NEF contours be based on a Peak Planning Day (PPD) scenario. Beyond this, there is no precise guidance as to the selection of specific input parameters. Varying input parameters can have a significant impact on the output. This inconsistency makes the resulting NEF contours unfit for regulatory purposes unless they can consistently and unbiasedly be reproduced. The process of modelling contours typically involves gathering data from different entities. Often those in charge of the selection of input parameters are unaware of the extent of changes on noise contours caused by even slight variations in the input data. In fact, multiple noise contours could be produced for the same airport scenario and while they may comply with the same methodology, there could be significant differences due to inconsistent selection of input data. This is a serious concern as inconsistencies of these tools have wide ranging implications and could create conflicts between various stakeholders. This paper aims to assess the impacts of different input parameters on noise contours by conducting a sensitivity analysis. The parameters analyzed in this research include the number of operations, runway distribution, operation type, operation time, stage length, flight track and aircraft model. Noise contours for a base airport scenario will be compared to scenarios with one modified input parameter per scenario. The resulting contours will be analysed to demonstrate the impact of variations in input parameters on noise contours. A better understanding of noise contours and the importance of consistent selection of input parameters will demonstrate the need for greater standardization of the modelling process. Furthermore, this research suggests a method of input variable selection that would improve the consistency and precision of noise contour mapping.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".