Yuting He, Shuqi Guo, Tiantian Shi
School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan, Ningxia 750021, China
Air pollution endangers human comfort, health and welfare or the environment. The practice of pollution prevention and control shows that establishing air quality prediction model, knowing the possible air pollution process in advance and taking corresponding control measures are one of the effective methods to reduce the harm of air pollution to human health and environment and improve ambient air quality. At present, WRF-CMAQ simulation system is commonly used to predict air quality, and the effect is not ideal. Therefore, firstly, this paper uses the grey correlation analysis model to study the correlation modeling between the variation characteristics of pollutant concentration and meteorological factors, and reasonably analyzes the meteorological conditions. Through the establishment of wavelet neural network prediction model, the pollutant concentration data are quantitatively analyzed, and then the single day concentration value of conventional pollutants in the future is predicted. The results show that WNN model is feasible to predict the concentration of air pollutants.
AQI, gray correlation, wavelet neural network, air pollution prediction
Yuting He, Shuqi Guo, Tiantian Shi. Research on Air Pollutant Concentration Change Based on Gray Correlation Snalysis and WNN. Academic Journal of Environment & Earth Science (2021) Vol. 3 Issue 4: 5-10. https://doi.org/10.25236/AJEE.2021.030402.
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