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2000 land-use regressions for road traffic noise predictions – how sample selection affects extrapolation weights

Staab, Jeroen and Schady, Arthur and Wolf, Kathrin and Behzadi, Sahar and Dallavalle, Marco and Weigand, Matthias and Lakes, Tobia and Taubenböck, Hannes (2022) 2000 land-use regressions for road traffic noise predictions – how sample selection affects extrapolation weights. In: Proceedings of the 24th International Congress on Acoustics, pp. 12-24. Proceedings of the 24th International Congress on Acoustics, 2022-10-24 - 2022-10-28, Gyeongju, Korea.

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Official URL: https://ica2022korea.org/data/Proceedings_A04.pdf

Abstract

The awareness that noise exposure is critical for human health is growing around the globe, and land-use regressions (LURs) are becoming a popular tool for producing noise exposure maps. One important factor for noise emissions is road traffic. The propagation in this regard is determined by the spatial layout of road infrastructure and the surrounding environment, respectively. LURs use geostatistical models and allow to extrapolate microphone measurements. In this study, we investigated whether models are prone to sampling artifacts. We used yearly averaged Lden simulations, compliant to the European noise directive 2002/49/EG, as input for 2000 virtual field campaigns. We permuted different sampling schemes (random, systematic, stratified) and sizes (n = 50, 100, 200, 500 to 1000) 100 times. The overall model performances varied substantially between 0.61 – 0.95 for R², 1.94 – 7.46 dB(A) for mean absolute error and 2.47 – 10.03 dB(A) for root mean squared error. Comparing the eventual model terms using variance analyses (ANOVA), we found significant differences between the sampling schemes for traffic information and land cover (e.g. vegetated surfaces) features. Simultaneously, less than half of the LURs’ weights differed significantly depending on the sampling size. Thus, our experiments give an in-depth view on the mechanics of LUR and their sensitivity with respect to sampled training data.

Item URL in elib:https://elib.dlr.de/187661/
Document Type:Conference or Workshop Item (Speech)
Title:2000 land-use regressions for road traffic noise predictions – how sample selection affects extrapolation weights
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Staab, JeroenUNSPECIFIEDhttps://orcid.org/0000-0002-7342-4440UNSPECIFIED
Schady, ArthurUNSPECIFIEDhttps://orcid.org/0000-0002-3078-9546UNSPECIFIED
Wolf, KathrinInstitute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, GermanyUNSPECIFIEDUNSPECIFIED
Behzadi, SaharInstitute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, GermanyUNSPECIFIEDUNSPECIFIED
Dallavalle, MarcoInstitute of Epidemiology, Helmholtz Zentrum München-German Research Centre for Environmental Health, Ingolstädter Landstrasse 1, 85764, Neuherberg, GermanyUNSPECIFIEDUNSPECIFIED
Weigand, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-5553-4152UNSPECIFIED
Lakes, TobiaHU BerlinUNSPECIFIEDUNSPECIFIED
Taubenböck, HannesUNSPECIFIEDhttps://orcid.org/0000-0003-4360-9126UNSPECIFIED
Date:2022
Journal or Publication Title:Proceedings of the 24th International Congress on Acoustics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 12-24
Status:Published
Keywords:Traffic noise, Exposure Assessment, Sensitivity Analysis
Event Title:Proceedings of the 24th International Congress on Acoustics
Event Location:Gyeongju, Korea
Event Type:international Conference
Event Start Date:24 October 2022
Event End Date:28 October 2022
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research, R - Geoscientific remote sensing and GIS methods, V - Digitaler Atlas 2.0
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Institute of Atmospheric Physics > Transport Meteorology
Deposited By: Staab, Jeroen
Deposited On:22 Nov 2022 20:07
Last Modified:24 Apr 2024 20:49

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