Health effects of noise and application of machine learning techniques as prediction tools in noise induced health issues: a systematic review

Abstract

Transportation noise is a widespread environmental problem in today’s society. The continuous movement of different vehicles on urban roads is the primary cause of such pollution. The review paper attempted to investigate numerous health issues caused by traffic noise exposure and how these health consequences were predicted using machine learning approaches such as structural equation modelling and artificial neural networks. Urban residents are exposed to such pollution during the day and night and have experienced its psychophysiological effects, whether knowingly or unknowingly. Furthermore, by reviewing numerous articles, this study attempted to investigate the relationship between socio-demographic factors and the effect of traffic noise, such as annoyance. The study also attempted to assess the relationships between various traffic noise-induced health issues such as headache, depression, sleeping problems, annoyance, blood pressure, and tiredness. Besides, evaluation and prediction play a key role to resolve any issue. Machine learning techniques such as structural equation modelling and artificial neural networks are useful tools that are rarely used in acoustic science and can be used to find associations as well as predict the effect of noise. The methodology and application of these two approaches are discussed in this study to provide a clear understanding of this application to the researchers working in this field.