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A neural network based model for urban noise prediction

A neural network based model for urban noise prediction
A neural network based model for urban noise prediction
Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.
0001-4966
1738-1746
Genaro, N.
f35a2663-d55d-4c46-9569-1cefff3d555b
Torija, A.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ramos-Ridao, A.
ccd2ff8b-030d-4674-bc46-555f8191fa93
Requena, I.
f81a936d-5af0-4f3b-8df3-35500b0857a4
Ruiz, D.P.
94601ea6-1ae9-44cf-ab47-8665a50152e0
Zamorano, M.
8d2192b0-26f3-4109-b5c1-13ed94b25054
Genaro, N.
f35a2663-d55d-4c46-9569-1cefff3d555b
Torija, A.
6dd0d982-fcd6-42b6-9148-211175fd3287
Ramos-Ridao, A.
ccd2ff8b-030d-4674-bc46-555f8191fa93
Requena, I.
f81a936d-5af0-4f3b-8df3-35500b0857a4
Ruiz, D.P.
94601ea6-1ae9-44cf-ab47-8665a50152e0
Zamorano, M.
8d2192b0-26f3-4109-b5c1-13ed94b25054

Genaro, N., Torija, A., Ramos-Ridao, A., Requena, I., Ruiz, D.P. and Zamorano, M. (2010) A neural network based model for urban noise prediction. Journal of the Acoustical Society of America, 128 (4), 1738-1746. (doi:10.1121/1.3473692).

Record type: Article

Abstract

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as a pollutant. Since then, most industrialized countries have enacted laws and local regulations to prevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangers of this type of pollution. In this context, urban planners need to have tools that allow them to evaluate the degree of acoustic pollution. Scientists in many countries have modeled urban noise, using a wide range of approaches, but their results have not been as good as expected. This paper describes a model developed for the prediction of environmental urban noise using Soft Computing techniques, namely Artificial Neural Networks (ANN). The model is based on the analysis of variables regarded as influential by experts in the field and was applied to data collected on different types of streets. The results were compared to those obtained with other models. The study found that the ANN system was able to predict urban noise with greater accuracy, and thus, was an improvement over those models. The principal component analysis (PCA) was also used to try to simplify the model. Although there was a slight decline in the accuracy of the results, the values obtained were also quite acceptable.

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More information

Accepted/In Press date: 6 July 2010
Published date: October 2010
Organisations: Acoustics Group

Identifiers

Local EPrints ID: 386647
URI: https://eprints.soton.ac.uk/id/eprint/386647
ISSN: 0001-4966
PURE UUID: 31bf72eb-a978-4e88-ace2-3f93ab67a6af
ORCID for A. Torija: ORCID iD orcid.org/0000-0002-5915-3736

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Date deposited: 03 Feb 2016 10:01
Last modified: 29 Aug 2019 00:35

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