Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model

Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model

Abstract Air pollution is an important issue affecting sustainable development in China, and accurate air quality prediction has become an important means of air pollution control. At present, traditional methods, such as deterministic and statistical approaches, have large prediction errors and can...

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Bibliographic Details
Main Authors: Kun Cai, Xusheng Zhang, Ming Zhang, Qiang Ge, Shenshen Li, Baojun Qiao, Yang Liu
Format: Article
Language:English
Published: BMC 2023-04-01
Series:Sustainable Environment Research
Subjects:
Air pollutant forecast
Missing time series data supplement
Deep learning
Informer
Autocorrelated Errors
Online Access:https://doi.org/10.1186/s42834-023-00175-w

Internet

https://doi.org/10.1186/s42834-023-00175-w

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