Variability of Noise Exposure Forecast Outputs Due to the Selection of Input Parameters
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».