03682nam a22005295i 4500 978-3-031-54667-9 DE-He213 20251030111736.0 cr nn 008mamaa 240321s2024 sz | s |||| 0|eng d 9783031546679 978-3-031-54667-9 10.1007/978-3-031-54667-9 doi TD891-894 TTA bicssc TEC001000 bisacsh TTA thema Albaji, Ali Othman. author. aut http://id.loc.gov/vocabulary/relators/aut 73679 Machine Learning for Environmental Noise Classification in Smart Cities [electronic resource] / by Ali Othman Albaji. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XVII, 170 p. 134 illus., 111 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Synthesis Lectures on Engineering, Science, and Technology, 2690-0327 We 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. Noise control. Machine learning. 55059 Environmental sciences Social aspects. 66050 Urban policy. 57466 Noise Control. Machine Learning. 55059 Environmental Social Sciences. 66053 Urban Policy. 57466 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031546662 Printed edition: 9783031546686 Printed edition: 9783031546693 Synthesis Lectures on Engineering, Science, and Technology, 2690-0327 https://doi.org/10.1007/978-3-031-54667-9 ZDB-2-SXSC 58551 58551