Garg, N. and Mangal, S. K. and Saini, P. K. and Dhiman, P. and Maji, S. (2015) Comparison of ANN and Analytical Models in Traffic Noise Modeling and Predictions. Acoustics Australia , 43 (2). pp. 179-189. ISSN 0814-6039

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Abstract

This paper demonstrates the applications of artificial neural networks to predict the equivalent continuous sound level (L-Aeq) and 10 Percentile exceeded sound level (L-10) generated due to traffic noise for various locations in Delhi. A Model based on back-propagation neural network was trained, validated, and tested using the measured data. The work shows that the model is able to produce accurate predictions of hourly traffic noise levels. A comparative study shows that neural networks out-perform the multiple linear regression models developed in terms of total traffic flow and equivalent traffic flow. The prediction model proposed in the study may serve as a vital tool for traffic noise forecasting and noise abatement measures to be undertaken for Delhi city.

Item Type:	Article
Subjects:	Acoustics
Divisions:	UNSPECIFIED
Depositing User:	Dr. Rajpal Walke
Date Deposited:	20 Sep 2016 07:01
Last Modified:	20 Sep 2016 07:01
URI:	http://npl.csircentral.net/id/eprint/1740

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