Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ISDA.2009.179 | ISDA |
Keywords | Field | DocType |
greater accuracy,urban noise,world health organization,paper reviews literature,artificial neural networks,mathematical model,different street location,noise level,artificial neural network,ann system,modeling environmental noise,classified noise,neural network,hidden markov models,mathematical models,predictive models,environmental noise,noise,neural nets,mathematical analysis,data collection,soft computing,neural networks,fuzzy logic | Noise classification,Computer science,Fuzzy logic,Noise level,Artificial intelligence,Soft computing,Mathematical model,Artificial neural network,Hidden Markov model,Machine learning,Environmental noise | Conference |
ISSN | ISBN | Citations |
2164-7143 | 978-0-7695-3872-3 | 1 |
PageRank | References | Authors |
0.45 | 8 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Genaro | 1 | 1 | 0.45 |
| A. Torija | 2 | 1 | 0.45 |
| A. Ramos | 3 | 1 | 0.45 |
| Ignacio Requena | 4 | 41 | 9.03 |
| D. P. Ruiz | 5 | 10 | 3.76 |
| M. Zamorano | 6 | 1 | 1.13 |