Comparison of Regression Model and Artificial Neural Network Model in Noise Prediction in a Mixed Area of Dhaka City
Date
2021-03-30
Journal Title
Journal ISSN
Volume Title
Publisher
Department of Civil and Environmental Engineering (CEE), Islamic University of Technology (IUT), Board Bazar, Gazipur, Bangladesh
Abstract
The equivalent noise levels regularly exceed acceptable limits within Dhaka city, the capital
of Bangladesh, especially in the mixed urban areas (where trips are generated to serve
commercial, residential, and industrial demands). The study aims to assess the noise level in
mixed urban areas, build noise prediction models and allow scopes for ensuring sustainable
environmental management. Two traffic noise prediction models were assessed: a regression
model and an artificial neural network (ANN) model to predict the equivalent noise level (Leq).
Traffic and noise level data were collected from two mixed urban areas, statistical analyses
were performed to describe the existing trends and to evaluate both model’s responses in
predicting equivalent noise level (Leq). The ANN model (coefficient of determination: 0.82)
showed better performance than the regression model (coefficient of determination: 0.70). The
predicted equivalent noise levels from the ANN model were compared to acceptable limits to
display the extent of noise pollution using GIS. The traffic noise models can assist in
environmental impact assessment to protect the communities susceptible to the adversities of
noise pollution.
Description
Supervised by
Ms. Tajkia Syeed Tofa,
Assistant Professor,
Department of Civil and Environmental Engineering,
Islamic University of Technology (IUT),
Board Bazar, Gazipur, Bangladesh.
Keywords
Noise pollution, Equivalent noise level, Prediction model, Regression, Artificial Neural Network.
Citation
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