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 ty...

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Bibliographic Details
Published in:The Journal of the Acoustical Society of America 2010-10, Vol.128 (4), p.1738-1746
Main Authors: Genaro, N., Torija, A., Ramos-Ridao, A., Requena, I., Ruiz, D. P., Zamorano, M.
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustics
City Planning
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Humans
Learning theory
Mathematical analysis
Mathematical models
Models, Theoretical
Neural networks
Neural Networks (Computer)
Noise
Noise - adverse effects
Noise: its effects and control
Physics
Pollution abatement
Principal Component Analysis
Reproducibility of Results
Urban Health
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Summary: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.
ISSN:0001-4966
1520-8524
DOI:10.1121/1.3473692