Study on prediction of atmospheric PM2.5 based on RBF neural network

被引：28
|
作者：
Zheng Haiming [1 ]
Shang Xiaoxiao [1 ]
机构：
[1] North China Elect Power Univ, Dept Mech Engn, Baoding 071003, Hebei, Peoples R China
来源：
2013 FOURTH INTERNATIONAL CONFERENCE ON DIGITAL MANUFACTURING AND AUTOMATION (ICDMA) | 2013年
关键词：
RBF Neural Network; Model Construction; Atmospheric PM2.5; Prediction;
D O I：
10.1109/ICDMA.2013.306
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Because of the varying concentration of atmospheric PM2.5 have strong nonlinear characteristics, the traditional forecast methods are difficult to make accurate prediction. In this paper the parameters included PM10, SO2, NO2, temperature, pressure, humidity, wind direction, wind speed are selected as the influence factors, and the prediction models based on RBF neural network are constructed. Then the model is used to predict the concentration of PM2.5 and compared with the classic BP network model. The result shows that the RBF neural network model has more advantages in the prediction of PM2.5.
引用
收藏
页码：1287 / 1289
页数：3
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