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

被引：0
|
作者：
Zhang, Shan [1 ]
Li, Xiaoli [1 ,2 ,3 ]
Li, Yang [4 ]
Mei, Jianxiang [1 ]
机构：
[1] Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
[2] Minist Educ, Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
[3] Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China
[4] Commun Univ China, Beijing 100024, Peoples R China
来源：
PROCEEDINGS OF THE 30TH CHINESE CONTROL AND DECISION CONFERENCE (2018 CCDC) | 2018年
基金：
中国国家自然科学基金;
关键词：
PM2.5 concentration prediction; wavelet Neural Network (WNN); air quality; computational intelligence (CI); MODEL;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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, PM2.5 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, NO2, SO2, PM10, PM2.5 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 (LSS VIM), wavelet Neural Network (WNN) model is constructed for short time prediction concentration of PM2.5. The experimental results show that the detection results based on WNN are more accurate, higher precision and strong self - learning ability.
引用
收藏
页码：5514 / 5519
页数：6
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