Chidananda Prasad Das, Shreerup Goswami, Bijay Kumar Swain, Mira Das
{"title":"Health effects of noise and application of machine learning techniques as prediction tools in noise induced health issues: a systematic review","authors":"Chidananda Prasad Das, Shreerup Goswami, Bijay Kumar Swain, Mira Das","doi":"10.1007/s10651-024-00629-3","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":50519,"journal":{"name":"Environmental and Ecological Statistics","volume":"54 1","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental and Ecological Statistics","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s10651-024-00629-3","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
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.