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

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Environmental and Ecological Statistics Pub Date : 2024-08-01 DOI:10.1007/s10651-024-00629-3
Chidananda Prasad Das, Shreerup Goswami, Bijay Kumar Swain, Mira Das
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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.

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噪声对健康的影响以及机器学习技术作为预测工具在噪声引起的健康问题中的应用:系统综述
交通噪音是当今社会普遍存在的环境问题。城市道路上各种车辆的不断行驶是造成这种污染的主要原因。这篇综述论文试图研究交通噪声暴露导致的众多健康问题,以及如何利用结构方程建模和人工神经网络等机器学习方法预测这些健康后果。城市居民在白天和夜晚都暴露在此类污染中,并在有意无意中感受到其对心理生理的影响。此外,通过查阅大量文章,本研究试图调查社会人口因素与交通噪音影响(如烦扰)之间的关系。本研究还试图评估头痛、抑郁、睡眠问题、烦扰、血压和疲劳等各种交通噪声引起的健康问题之间的关系。此外,评估和预测对解决任何问题都起着关键作用。结构方程建模和人工神经网络等机器学习技术是声学科学中很少使用的有用工具,可用于发现关联和预测噪声的影响。本研究讨论了这两种方法的方法论和应用,以便让这一领域的研究人员清楚地了解这种应用。
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来源期刊
Environmental and Ecological Statistics
Environmental and Ecological Statistics 环境科学-环境科学
CiteScore
5.90
自引率
2.60%
发文量
27
审稿时长
>36 weeks
期刊介绍: Environmental and Ecological Statistics publishes papers on practical applications of statistics and related quantitative methods to environmental science addressing contemporary issues. Emphasis is on applied mathematical statistics, statistical methodology, and data interpretation and improvement for future use, with a view to advance statistics for environment, ecology and environmental health, and to advance environmental theory and practice using valid statistics. Besides clarity of exposition, a single most important criterion for publication is the appropriateness of the statistical method to the particular environmental problem. The Journal covers all aspects of the collection, analysis, presentation and interpretation of environmental data for research, policy and regulation. The Journal is cross-disciplinary within the context of contemporary environmental issues and the associated statistical tools, concepts and methods. The Journal broadly covers theory and methods, case studies and applications, environmental change and statistical ecology, environmental health statistics and stochastics, and related areas. Special features include invited discussion papers; research communications; technical notes and consultation corner; mini-reviews; letters to the Editor; news, views and announcements; hardware and software reviews; data management etc.
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