Modeling environmental noise using artificial neural networks

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

Abstract

Since 1972, when the World Health Organization
(WHO) classified noise as a pollutant, most industrialized countries
have enacted laws or local regulations that regulate noise
levels. Many scientists have tried to model urban noise, but
the results have not been as good as expected because of the
reduced number of variables. This paper describes artificial
neural networks (ANN) to model urban noise. This model
was applied to data collected at different street locations in
Granada, Spain. The results were compared to those obtained
with mathematical models. It was found that the ANN system
was able to predict noise with greater accuracy, and therefore it
was an improvement on these models. Furthermore, this paper
reviews literature describing other research studies that also used
soft computing techniques to model urban noise.

Citation

Genaro, N., Torija Martinez, A., Ramos, A., Requena, I., Ruiz, D., & Zamorano, M. (2009, November). Modeling environmental noise using artificial neural networks. Presented at 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy

• Presentation Conference Type: Other
• Conference Name: 2009 Ninth International Conference on Intelligent Systems Design and Applications
• Conference Location: Pisa, Italy
• Start Date: Nov 30, 2009
• End Date: Dec 2, 2009
• Online Publication Date: Dec 28, 2009
• Publication Date: Dec 28, 2009
• Deposit Date: Dec 2, 2019
• Journal: ISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications
• Print ISSN: 2164-7143
• Electronic ISSN: 2164-7151
• Pages: 215-219
• Book Title: 2009 Ninth International Conference on Intelligent Systems Design and Applications
• ISBN: 9781424447350
• DOI: https://doi.org/10.1109/ISDA.2009.179
• Publisher URL: http://dx.doi.org/10.1109/ISDA.2009.179
• Additional Information: Event Type : Conference; Funders : Consejeria de Innovacion, Ciencia y Economia de la Junta de Andalucia;Spanish Government; Grant Number: P07-TIC-0291; Grant Number: P08-RNM-03584; Grant Number: TIN2006-15041-C04-01; Grant Number: DGI-MCYT