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Automated noise modelling using a triangulated terrain model

Authors :
Nadine Hobeika
Laurens van Rijssel
Maarit Prusti
Constantijn Dinklo
Denis Giannelli
Balázs Dukai
Arnaud Kok
Rob van Loon
René Nota
Jantien Stoter
Source :
Geo-spatial Information Science, Pp 1-21 (2023)
Publication Year :
2023
Publisher :
Taylor & Francis Group, 2023.

Abstract

Noise simulations are an important part of noise studies that investigate the impact of noise sources on the environment. In noise simulation, noise levels at receiver points are calculated based on the noise propagation paths between the receiver and source points. These paths are derived from the height of the terrain. In current calculation approaches implemented in noise simulation software, 3D polylines are used as input to describe the height of the terrain. These 3D polylines are semi-automatically generated to meet the highly demanding computing performance of simulation software. In addition, previous research showed that the reconstruction of appropriate height lines as used in noise simulation is very difficult to automate, if not impossible As a solution, this research investigates how noise propagation paths between receiver and source points can directly be generated from a Triangulate Irregular Network (TIN) without creating the height lines. This would allow us to use the automatically generated TIN as input for noise simulation instead of the height lines. In addition, a TIN enables better control of the quality of the data than height lines do. This study uses the 3D noise modeling guidelines of Common Noise Assessment Methods in Europe (CNOSSOS-EU). Algorithms have been developed and implemented in a prototype to generate and validate the paths between receiver and source points using a TIN that includes the buildings as well as the noise absorption properties of the terrain. The prototype is successfully tested on two scenarios from the Netherlands. Since CNOSSOS-EU guidelines were used, the prototype is applicable to the entire European Union and can be the first step in improving the automation of 3D noise modeling using currently available techniques and data.

Details

Language :
English
ISSN :
10095020 and 19935153
Database :
Directory of Open Access Journals
Journal :
Geo-spatial Information Science
Publication Type :
Academic Journal
Accession number :
edsdoj.5b65e59fac2c4ca4b06a68650e2d8db9
Document Type :
article
Full Text :
https://doi.org/10.1080/10095020.2023.2270520