Artificial Neural Network Modelling of Traffic Noise Induced Annoyance Amongst Exposed Population

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IJEP 42(9): 1042-1050 : Vol. 42 Issue. 9 (September 2022)

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Chidananda Prasad Das1, Smita Rath2, Bijay Kumar Swain3, Shreerup Goswami4 and Mira Das1*

1. Siksha ‘O’ Anusandhan (Deemed to be University), Environmental Science Programme, Department of Chemistry, ITER, Bhubaneswar – 751 030, Odisha, India
2. Siksha ‘O’ Anusandhan (Deemed to be University), Department of Computer Science and Engineering, ITER, Bhubaneswar – 751 030, Odisha, India
3. District Institute of Education and Training (DIET), Bhadrak, Agarpada – 756 115, Odisha, India
4. Utkal University, Department of Geology, Vanivihar, Bhubaneswar – 751 004, Odisha, India

Abstract

In the current situation, traffic noise and annoyance are a matter of concern. The current study aimed to predict annoyance levels using artificial neural network (ANN) multi-layer perceptron network (MLPN) by adding five parameters, such as hours of noise exposure, qualifications, marital status and age of the respondents. This study included 60 persons (30 men and 30 women). The best ANN model was chosen by comparing the mean square error and root mean square error values of 2500 different architectures (500 architectures for each neuron, that is 1-5) with constant input, output and hidden layers with varying neurons (1-5). The architecture of the best model was ‘5 inputs®1 hidden layer (5 neurons) ® 1 output’ with minimum MSE (0.014658) and RMSE (0.12107) values. The model’s performance was determined by its relative error, which was 0.198. Hours of exposure were shown to be the most important predictor of annoyance, with a score of 0.467, followed by qualification with a score of 0.418, while age was found to be the least important predictor. According to the correlation analysis, there was a high positive link between annoyance and hours, with a Pearson correlation value of 0.758, followed by qualifications, with a Pearson correlation value of 0.669.

Keywords

Annoyance, Artificial neural network, Multi-layer perceptron, Mean square error, Root mean square error

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