Universiti Teknologi Malaysia Institutional Repository

3D kriging interpolation for traffic noise visualization: Designing noise observation points and valuation of spatial interpolation accuracy.

Wickramathilaka, N. and Ujang, U. (2023) 3D kriging interpolation for traffic noise visualization: Designing noise observation points and valuation of spatial interpolation accuracy. In: International Graduate Conference of Built Environment and Surveying 2023, GBES 2023, 17 September 2023 - 18 September 2023, Johor Bahru, Johor, Malaysia - Hybrid.

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Official URL: http://dx.doi.org/10.1088/1755-1315/1274/1/012001

Abstract

Identifying the risk of traffic noise is vital in minimizing traffic noise pollution in urban areas. As noise travels in every direction, 3D visualization of traffic noise is essential, which involves visualising traffic noise along the facades of buildings. A standard traffic noise model is necessary to calculate traffic noise levels, as several factors affect traffic noise. Moreover, designing noise observation points in 3D and spatial interpolation play significant roles in 3D noise visualisation. Therefore, this study demonstrates the results by elaborating on the spatial interpolation and designing noise observation points. A noise observation point consists of four parameters in 3D space. Generally, Inverse Distance Weighted (IDW), Triangular Irregular Network (TIN), and Kriging do not support the interpolation of four parameters in 3D. However, 3D Kriging in Empirical Bayesian Kriging provides significant opportunities to interpolate noise levels in 3D. However, the elements of the function of spatial interpolations are vital for accuracy. The 3D Kriging uses different variograms according to semivariance. This variogram directly impacts the weighting factor of 3D Kriging. Therefore, this study develops a comparison to identify the impact of different variograms on the accuracy of 3D Kriging interpolation on traffic noise.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:noise, 3D visualization, Triangular Irregular Network (TIN), Kriging in Empirical Bayesian.
Subjects:T Technology > TH Building construction > TH434-437 Quantity surveying
Divisions:Built Environment
ID Code:108382
Deposited By: Muhamad Idham Sulong
Deposited On:01 Nov 2024 02:40
Last Modified:01 Nov 2024 02:40

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