Prediction of Urban PM2.5 Concentration Based on Wavelet Neural Network

The main objective of this study is to determine the more appropriate computational intelligence (CI) model for the prediction of air pollutants in urban areas. In this paper, in order to emphasize the importance of short-term air quality (AQ) prediction, PM
_2.5
is used as an example to evaluate the concentration of pollutants using a variety of CI methods and tools. According to the data of air quality monitoring stations, the main air pollutants O
₃
, CO, NO
₂
, SO
₂
, PM
₁₀
, PM
_2.5
and two kinds of meteorological factors temperature and humidity are selected as influencing factors. Comparing with the model of extreme learning machine (ELM), fuzzy neural network (FNN) and least squares support vector machine (LSSVM), wavelet Neural Network (WNN) model is constructed for short time prediction concentration of PM
_2.5
. The experimental results show that the detection results based on WNN are more accurate, higher precision and strong self - learning ability.