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12 January 2018 Environmental noise forecasting based on support vector machine
Yumei Fu, Xinwu Zan, Tianyi Chen, Shihan Xiang
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Proceedings Volume 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 106201K (2018) https://doi.org/10.1117/12.2295298
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China
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.
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Yumei Fu, Xinwu Zan, Tianyi Chen, and Shihan Xiang "Environmental noise forecasting based on support vector machine", Proc. SPIE 10620, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 106201K (12 January 2018); https://doi.org/10.1117/12.2295298
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KEYWORDS
Data modeling
Artificial neural networks
Statistical modeling
Analytical research
Environmental sensing
Factor analysis
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