A neural network based model for urban noise prediction.

Noise is a global problem. In 1972 the World Health Organization (WHO) classified noise as apollutant. Since then, most industrialized countries have enacted laws and local regulations toprevent and reduce acoustic environmental pollution. A further aim is to alert people to the dangersof this type of pollution. In this context, urban planners need to have tools that allow them toevaluate 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 paperdescribes a model developed for the prediction of environmental urban noise using Soft Computingtechniques, namely Artificial Neural Networks (ANN). The model is based on the analysis ofvariables regarded as influential by experts in the field and was applied to data collected on differenttypes of streets. The results were compared to those obtained with other models. The study foundthat the ANN system was able to predict urban noise with greater accuracy, and thus, was animprovement over those models. The principal component analysis (PCA) was also used to try tosimplify the model. Although there was a slight decline in the accuracy of the results, the valuesobtained were also quite acceptable