Noise estimation using an artificial neural network in the urban area of Jaen, Cajamarca

Abstract

Jaen is a city in constant urban growth which generates an increase in vehicular traffic and active noise pollution. The research presents the development of an artificial neural network (ANN) to estimate the noise produced by vehicular traffic in the urban area of the city. Consequently, information was collected from two investigations coded as T1 and T2, for which a matrix of 10 variables was elaborated with 210 and 273 data respectively. Random random sampling was performed to divide the data matrix into 80% (training) and 20% (validation). Weka software and the multi-layer perceptron (MLP) training algorithm were used to model the ANN. An ANN for T1 with 6-19-1 architecture and an ANN for T2 with 6-15-1 architecture were obtained. The performance of the ANNs was evaluated using the correlation coefficient (R), coefficient of determination (R2) and root mean square error (RMSE). The results show that the MLP networks are able to estimate the sound pressure level with values of R=0.9927, R2=0.9854 and RMSE=0.7313 for T1, R=0.9989, R2=0.9978, and RMSE=0.1515 for T2.