ANN Modelling of Traffic Noise in Quilon-Kochi Highway
Author(s):
Lekshmi.S, Nithya.Kurup, Dr. Priya.K.L
Keywords:
Back-Propogation, Model, Noise Pollution, Statistical analysis, Vehicular Traffic
Abstract
Noise is one of the most prevalent sources of environmental pollution and is considered as the second largest pollution after air pollution. As there are many factors which contributes to noise pollution, the major contributor among all being the vehicular traffic noise. It is considered as one of the most invasive type of pollution and often the most intrusive of all. The trend of noise pollution modelling varies from the smart result of classic regressive models to the performance of many assessment models based on mathematical expression, genetic algorithms and neural networks. In this study, feed forward back propagation neural network has been developed to predict vehicular traffic noise in urban area. The study area is an ancient trade town having connections with major districts and states. NH-66 road stretch with 6 sampling locations each 500m intervals were selected for the present study. The proposed ANN model has been used to predict the equivalent continuous sound level (Leq) in dB (A). The model input parameters are the characteristics of the vehicular traffic flows (traffic volume, percentage of heavy vehicles and traffic speed). A comparison between the field measurement and the predicted Leq from neural network approach and the regression analysis was done. The results show how the neural network approach provides better performance than the classical solution based on statistical analysis.
Article Details
Unique Paper ID: 146304

Publication Volume & Issue: Volume 4, Issue 12

Page(s): 230 - 235
Article Preview & Download


Share This Article

Join our RMS



Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Latest Publication

Call For Paper

Volume 10 Issue 10

Last Date for paper submitting for March Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers