Secondary Modeling of Pollutant Concentration Prediction Based on Deep Neural Networks with Federal Learning

In the new century,along with the rapid development of Chinese economy,air pollution in many areas of China is relatively serious,while the government is paying more and more attention to air pollution,and its efforts to control air pollution are increasing.Currently,six pollutants that have the gre...

Full description

Saved in:
Bibliographic Details
Published inJi suan ji ke xue Vol. 49; p. 932
Main Authors Qian, Dong-wei, Cui, Yang-guang, Wei, Tong-quan
Format Journal Article
LanguageChinese
Published Chongqing Guojia Kexue Jishu Bu 01.01.2022
Subjects
Air pollution
Air quality
Artificial neural networks
Chemical reactions
Economic development
Environmental monitoring
Machine learning
Modelling
Neural networks
Nitrogen dioxide
Pollutants
China
Online AccessGet full text

Cover

More Information
Summary:In the new century,along with the rapid development of Chinese economy,air pollution in many areas of China is relatively serious,while the government is paying more and more attention to air pollution,and its efforts to control air pollution are increasing.Currently,six pollutants that have the greatest impact on China's air quality are O3,SO2,NO2,CO,PM10,PM2.5.Therefore,predicting and forecasting the concentrations of the six pollutants and making corresponding control adjustments in time have become the urgent needs to protect the health of residents and build a beautiful China.At present,the mainstream solution for pollutant prediction is WRF-CMAQ prediction system,which is based on two parts,physical and chemical reaction of pollutants and meteorological simulation.However,due to the current research on the generation mechanism of pollutants such as ozone is still on the way,the prediction of WRF-CMAQ model has large errors.Therefore,this paper adopts a deep neural network for secondary modeling of pollu
ISSN:1002-137X