Uncertainty in Noise Mapping: Comparing a Probabilistic and a Fuzzy Set Approach

Muer, Tom De; Botteldooren, Dick

doi:10.1007/3-540-44967-1_27

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Apart from the techniques proposed by the GUM, there is the fuzzy logic, which defines values in a real closed interval and applies functions with real numbers to its extension with fuzzy numbers [7]. The biggest problem of fuzzy logic as a tool to determine the uncertainty is the computational cost and complexity [8].…”

Section: General Methods To Calculate the Uncertaintymentioning

confidence: 99%

Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

Ausejo¹,

Recuero²,

Asensio³

et al. 2011

Acta Acustica united with Acustica

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SummaryThe influence of applying European default traffic values to the making of a noise map was evaluated in a typical environment like Palma de Mallorca. To assess these default traffic values, a first model has been created and compared with measured noise levels. Subsequently a second traffic model, improving the input data used for the first one, has been created and validated according to the deviations. Different methodologies were also examined for collecting model input data that would be of higher quality, by analysing the improvement generated in the reduction in the uncertainty of the noise map introduced by the road traffic noise emission.

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Section: General Methods To Calculate the Uncertaintymentioning

confidence: 99%

Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

Ausejo¹,

Recuero²,

Asensio³

et al. 2011

Acta Acustica united with Acustica

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“…The aim of this study is therefore to analyze the noise levels at various locations within the Federal University of Technology, Owerri using Geographic Information System (GIS). According to De Muer and Botteldooren (2003), noise is mapped to display the spatiotemporal distribution in the studied environment. This study mapped the spatial distribution of noise using the ArcGIS Inverse Distance Weighted Model (IDW).…”

Section: Introductionmentioning

confidence: 99%

Geo-Spatial Analysis of Noise Levels in a University Community

2022

BJES

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Noise is identified as a disturbance that could induce different behavioural responses. Its exposure for a long period of time has been an issue of concern. Increasing noise levels is not unusual especially with booming developments and innovations in the University environment. This study investigated noise pollution level (NPL) and its spatial distribution within the Campus of Federal University of Technology, Owerri Nigeria. A digital sound level meter-Extech 407730 was used to measure noise level at the sampling points namely, Student affairs unit, SEET head round-about, School park, Senate round-about and FUTO market designated as SP1,SP2,SP3,SP4 and SP5 respectively. A global positioning system (GPS)-NAVA 300 was used to record the GPS coordinates of the sampling noise hotspots in the university community. The Arc GIS software was used to assess the geospatial mapping and distribution of noise within the study areas. Results revealed that the noise levels in the school campus reached a peak of 75.7dB (A) in FUTO market on day 4 during the hours of 12pm – 1pm. Results of geospatial mapping of the noise level in the study area also revealed that noise level exceeds the maximum permissible limit ranging from 40dB(A) to 50dB(A). Therefore, it was recommended that public enlightenment programs such as workshops and seminars on the health hazards connected with noise pollution within the campus should be held regularly, also, control of noise through the use of sound proof doors, walls, and ceilings that can be installed while erecting buildings within the university community.

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“…..). The uncertainty of the noise levelcalculations and models has been widely studied [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15], and is out of the scope of this paper.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in Noise Maps Isolines: The Effect of the Sampling Grid

Asensio¹,

Ruiz²,

Pavón³

et al. 2011

Acta Acustica united with Acustica

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Noise maps results are usually presented as contour graphs or isoline curves, which describe the sound levels as functions of spatial location. These maps are added to Geographic Information Systems (GIS), allowing sound levelevaluation as afunction of the continuous coordinates x and y,for agiven height above ground. Although the outcome of the system is acontinuous variable, the calculations that allowits evaluation are obtained from discrete points structured in ac alculation grid. This grid is created by the application of spatial sampling techniques. Using spatial interpolation tools (IDW,krigging. ..), values are assigned to the locations in which acoustic calculations have not been performed. The application of sampling and interpolation techniques (the type of grid, its density,t he interpolation algorithms. ..)c ontributes to the uncertainty of the results. This paper describes a calculation method to quantify the uncertainty associated to the spatial sampling and interpolation processes. Despite most of the previous literature refers to the uncertainty in anoise map as the uncertainty of the receivers' results (output of the noise model), in this approach we propose also including the contributions derivedbythe interpolation and classification processes, so that the uncertainty must be estimated only for the locations on the contour lines.

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Uncertainty in Noise Mapping: Comparing a Probabilistic and a Fuzzy Set Approach

Cited by 6 publications

References 6 publications

Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

Geo-Spatial Analysis of Noise Levels in a University Community

Uncertainty in Noise Maps Isolines: The Effect of the Sampling Grid

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