03400nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001400153072001600167072002300183072001500206100008100221245012400302250001800426264006900444300006800513336002600581337002600607338003600633347002400669490007500693520167600768650001902444650002202463650004402485650001802529650001902547650002202566650003502588650001802623710003402641773002602675776003602701776003602737776003602773830007502809856004602884978-3-031-54667-9DE-He21320251030111736.0cr nn 008mamaa240321s2024    sz |    s    |||| 0|eng d  a97830315466797 a10.1007/978-3-031-54667-92doi 4aTD891-894 7aTTA2bicssc 7aTEC0010002bisacsh 7aTTA2thema1 aAlbaji, Ali Othman.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aMachine Learning for Environmental Noise Classification in Smart Citiesh[electronic resource] /cby Ali Othman Albaji.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXVII, 170 p. 134 illus., 111 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSynthesis Lectures on Engineering, Science, and Technology,x2690-0327  aWe present a Machine Learning (ML) approach to monitoring and classifying noise pollution. Both methods of monitoring and classification have been proven successful. MATLAB and Python code was generated to monitor all types of noise pollution from the collected data, while ML was trained to classify these data. ML algorithms showed promising performance in monitoring the different sound classes such as highways, railways, trains and birds, airports and many more. It is observed that all the data obtained by both methods can be used to control noise pollution levels and for data analytics. They can help decision making and policy making by stakeholders such as municipalities, housing authorities and urban planners in smart cities. The findings indicate that ML can be used effectively in monitoring and measurement. Improvements can be obtained by enhancing the data collection methods. The intention is to develop more ML platforms from which to construct a less noisy. The second objective of this study was to visualize and analyze the data of 18 types of noise pollution that have been collected from 16 different locations in Malaysia. All the collected data were stored in Tableau software. Through the use of both qualitative and quantitative measurements, the data collected for this project was then combined to create a noise map database that can help smart cities make informed decisions. In addition, this book: Machine learning-based sound classifier for environmental noise Qualitative analysis of community perceptions based on a noise pollution survey Create an interactive web dashboard and data warehousing for intelligent analytics reporting. 0aNoise control. 0aMachine learning. 0aEnvironmental sciencesxSocial aspects. 0aUrban policy.14aNoise Control.24aMachine Learning.24aEnvironmental Social Sciences.24aUrban Policy.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303154666208iPrinted edition:z978303154668608iPrinted edition:z9783031546693 0aSynthesis Lectures on Engineering, Science, and Technology,x2690-032740uhttps://doi.org/10.1007/978-3-031-54667-9