Modelling and Prediction of Road Transportation Noise Pollution in Some Capital Cities in Eastern Nigeria by Use of Artificial Neural Network

Effiong O Obisung ,Aniefiok O Akpan ,Ubon E Asuquo

doi:10.7176/jees/11-10-06

Abstract

The study attempts to model and predict road transportation noise pollution in five capital cities in Eastern Nigeria. The capital cities are Calabar, Uyo, Umuahia, Owerri and Port Harcourt. Feed-forward neural network (FNN) with negative back-propagation algorithm was used to do this. The software used was NeuroXL. The ability of this software to handle multiple non-linear relationships makes it ideall y suited for this work. The input data used were total road traffic volume, road traffic mix, road traffic noise pollution response data, and distances from road centre-line to measurement points. The output data used was A-weighted energy mean sound level (L A eq). Models based on this negative back- propagation neural network were trained, validated and tested using data collected. The performance of the model was tested by an error measure, root mean square error (RMSE). RMSE is low as expected, ranges from 1.007 - 1.814, showing that the model is good for the prediction of road traffic noise data. The correlation between observed and predicted noise levels (LAeq) was also obtained, and ranges between +0.757 to +0.974, showing that there is no significant difference between observed and predicted noise levels, thereby, proving the model accurate and reliable. Keywords : Artificial neural network, back-propagation, road traffic noise modeling, road traffic noise prediction, NeuroXL software DOI: 10.7176/JEES/11-10-06 Publication date: October 31 st 2021

Highlights

Nigerian urban dwellers are excessively exposed to severe environmental/city noise pollution
Fifty (50) sites were chosen from road transportation high noise pollution zones, where heavy road transportation volume and dense traffic mix are experienced, on daily basis to serve as study group, while 50 sites were from low noise zones to serve as control group
3.0 Results The findings of this study are summarized in tables 6 – 10, 13 and Figs. 3 – 7

Summary

Introduction

Nigerian urban dwellers are excessively exposed to severe environmental/city noise pollution. The most disturbing city noise source, as generally established in the developing and developed urban communities being road transportation, as noise from it causes a lot of socio-psychological and physiological problems such as annoyance, sleeplessness, hearing loss, communication disturbances, speech intelligibility, cardiovascular disorders and other health problems [1 - 9]. The heterogeneous nature of urban environments, coupled with the characteristics of road transportation noise, their spatial, temporal and spectral variability, makes the matter of modeling and prediction of road transportation pollution a very complex and non-linear problem, to which the application of artificial neural networks becomes imperative. Artificial neural networks (ANNs) are widely used in road transportation noise modeling and prediction as a preference to more conventional statistical techniques, because ANNs are non-linear, relatively insensitive to noise data, perform reasonably well when limited data are available, and provide flexibility, accuracy and fault tolerance in changing environments [9,10,11,12,13,14,15,16,17]

Methods

Results

Discussion

Conclusion