A neural network based model for urban noise prediction.

Genaro, N, Torija Martinez, AJ ORCID: https://orcid.org/0000-0002-5915-3736, Ramos-Ridao, A, Requena, I, Ruiz, DP and Zamorano, M 2010, 'A neural network based model for urban noise prediction.' , The Journal of the Acoustical Society of America (JASA), 128 (4) , pp. 1738-1746.

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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.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: The Journal of the Acoustical Society of America (JASA)
Publisher: Acoustical Society of America
ISSN: 1520-8524
Related URLs:
Funders: Consejeria de Innovacion, Ciencia y Economia de la Junta de Andalucia, Consejeria de Innovacion, Ciencia y Economia de la Junta de Andalucia, Spanish Government, Spanish Goverment
Depositing User: Dr Antonio J Torija Martinez
Date Deposited: 02 Dec 2019 15:41
Last Modified: 02 Dec 2019 15:45
URI: http://usir.salford.ac.uk/id/eprint/53233

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