• Volume/Page
  • Keyword
  • DOI
  • Citation
  • Advanced
   
 
 
 

You are not logged in You are logged out of this journal. Log In

Journal of the Acoustical Society of America / Volume 128 / Issue 4 / NOISE: ITS EFFECTS AND CONTROL [50]

A neural network based model for urban noise prediction

J. Acoust. Soc. Am. Volume 128, Issue 4, pp. 1738-1746 (2010); (9 pages)

N. Genaro1, A. Torija2, A. Ramos-Ridao3, I. Requena1, D. P. Ruiz2, and M. Zamorano3

1Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
2Department of Applied Physics, University of Granada, 18071 Granada, Spain
3Department of Civil Engineering, University of Granada, 18071 Granada, Spain

Full Text: Read Online (HTML) | Download PDF | Buy PDF (US$30) | View Cart
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.

© 2010 Acoustical Society of America

ACKNOWLEDGMENTS

This research was partially funded by the projects P07-TIC-02913, P07-TIC-03269, CICE, the Andalusian Regional Government, and TIN2006-15041-C04-01, DGI-MCYT, Spain.

Article Outline

  1. INTRODUCTION
  2. ARTIFICIAL NEURAL NETWORKS
  3. A NEURAL NETWORK FOR URBAN NOISE PREDICTION
    1. Data collection process
    2. Artificial neural network for noise level prediction
    3. Comparison of our neural network model and other predictive models
    4. Input reduction through Principal Component Analysis (PCA)
  4. CONCLUSIONS

RELATED DATABASES

To view database links for this article, you need to log in.

KEYWORDS and PACS

Keywords

neural nets, noise (working environment), noise pollution, principal component analysis

PACS

  • 43.50.Rq

    Environmental noise, measurement, analysis, statistical characteristics

  • 43.58.Ta

    Computers and computer programs in acoustics

ARTICLE DATA

History
Received 07 May 2009
Accepted 06 Jul 2010
Revised 21 Apr 2010
Digital Object Identifier
http://dx.doi.org/10.1121/1.3473692

PUBLICATION DATA

ISSN

0001-4966 (print)  

Publisher

$abstract.society.value is a Member of CrossRef Acoustical Society of America

For access to fully linked references, you need to log in.

For access to citing articles, you need to log in.


Figures (6) Tables (4)

Access to article objects (figures, tables, multimedia) requires a subscription; log in to view available files.
(Access to supplementary files, where available, is free for this journal.)

Access to article objects (figures, tables, multimedia) requires a subscription; log in to view available files.
(Access to supplementary files, where available, is free for this journal.)



Close

Your Recent History Recent History Help

Recently Viewed

  1. A neural network based model for urban noise prediction

Home Publications Meetings Contact ASA Site Map Back to Top

Copyright © Acoustical Society of America

close