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Proceedings Paper

Environmental noise forecasting based on support vector machine
Author(s): Yumei Fu; Xinwu Zan; Tianyi Chen; Shihan Xiang
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Paper Abstract

As an important pollution source, the noise pollution is always the researcher’s focus. Especially in recent years, the noise pollution is seriously harmful to the human beings’ environment, so the research about the noise pollution is a very hot spot. Some noise monitoring technologies and monitoring systems are applied in the environmental noise test, measurement and evaluation. But, the research about the environmental noise forecasting is weak. In this paper, a real-time environmental noise monitoring system is introduced briefly. This monitoring system is working in Mianyang City, Sichuan Province. It is monitoring and collecting the environmental noise about more than 20 enterprises in this district. Based on the large amount of noise data, the noise forecasting by the Support Vector Machine (SVM) is studied in detail. Compared with the time series forecasting model and the artificial neural network forecasting model, the SVM forecasting model has some advantages such as the smaller data size, the higher precision and stability. The noise forecasting results based on the SVM can provide the important and accuracy reference to the prevention and control of the environmental noise.

Paper Details

Date Published: 12 January 2018
PDF: 6 pages
Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106201K (12 January 2018); doi: 10.1117/12.2295298
Show Author Affiliations
Yumei Fu, Chongqing Univ. (China)
Xinwu Zan, Chongqing Univ. (China)
Tianyi Chen, China Aerodynamics Research and Development Ctr. (China)
Shihan Xiang, Chongqing Univ. (China)


Published in SPIE Proceedings Vol. 10620:
2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology
Guohai Situ; Xun Cao; Wolfgang Osten; Liquan Dong, Editor(s)

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