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020 _a9783031546679
_9978-3-031-54667-9
024 7 _a10.1007/978-3-031-54667-9
_2doi
050 4 _aTD891-894
072 7 _aTTA
_2bicssc
072 7 _aTEC001000
_2bisacsh
072 7 _aTTA
_2thema
100 1 _aAlbaji, Ali Othman.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_973679
245 1 0 _aMachine Learning for Environmental Noise Classification in Smart Cities
_h[electronic resource] /
_cby Ali Othman Albaji.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVII, 170 p. 134 illus., 111 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
520 _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.
650 0 _aNoise control.
650 0 _aMachine learning.
_955059
650 0 _aEnvironmental sciences
_xSocial aspects.
_966050
650 0 _aUrban policy.
_957466
650 1 4 _aNoise Control.
650 2 4 _aMachine Learning.
_955059
650 2 4 _aEnvironmental Social Sciences.
_966053
650 2 4 _aUrban Policy.
_957466
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031546662
776 0 8 _iPrinted edition:
_z9783031546686
776 0 8 _iPrinted edition:
_z9783031546693
830 0 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
856 4 0 _uhttps://doi.org/10.1007/978-3-031-54667-9
912 _aZDB-2-SXSC
999 _c58551
_d58551