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Prediction Model of Urban Environmental Noise in Smart Environment

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Smart Cities: Big Data Prediction Methods and Applications

Abstract

With the continuous progress of modern urbanization, urban noise pollution is also increasing. Noise pollution has been disturbing the normal life of people, and serious noise pollution may even affect people’s health. For this reason, it is necessary to make an effective noise prediction. Noise prediction uses historical data from noise monitoring points to predict future noise values, helping to provide effective noise regulation. In this chapter, the RF, BFGS, and GRU models are used to conduct feasibility studies for noise prediction. These three models are used to predict the public noise, traffic noise, and neighborhood noise. Through comprehensive comparative analysis of the experimental results, it can be concluded that the noise prediction performance of the BFGS model is the best in this experiment. Neighborhood noise is the most predictable among the three types of noise data.

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Authors and Affiliations

  1. School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China

    Hui Liu

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  1. Hui Liu
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Liu, H. (2020). Prediction Model of Urban Environmental Noise in Smart Environment. In: Smart Cities: Big Data Prediction Methods and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-2837-8_10

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  • DOI: https://doi.org/10.1007/978-981-15-2837-8_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2836-1

  • Online ISBN: 978-981-15-2837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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