A neural network based model for urban noise prediction.

Genaro, N; Torija Martinez, AJ; Ramos-Ridao, A; Requena, I; Ruiz, DP; Zamorano, M

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.

Citation

Genaro, N., Torija Martinez, A., Ramos-Ridao, A., Requena, I., Ruiz, D., & Zamorano, M. (2010). A neural network based model for urban noise prediction. ˜The œJournal of the Acoustical Society of America (Online), 128(4), 1738-1746. https://doi.org/10.1121/1.3473692

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2010
• Online Publication Date: Oct 18, 2010
• Publication Date: Oct 18, 2010
• Deposit Date: Dec 2, 2019
• Journal: The Journal of the Acoustical Society of America (JASA)
• Print ISSN: 0001-4966
• Volume: 128
• Issue: 4
• Pages: 1738-1746
• DOI: https://doi.org/10.1121/1.3473692
• Publisher URL: https://doi.org/10.1121/1.3473692
• Related Public URLs: https://asa.scitation.org/journal/jas
• Additional Information: Funders : Consejeria de Innovacion, Ciencia y Economia de la Junta de Andalucia;Spanish Government;Spanish Goverment; Grant Number: P07-TIC-02913; Grant Number: P07-TIC-03269; Grant Number: TIN2006-15041-C04-01; Grant Number: DGI-MCYT