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

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Bibliographic Details
Main Authors: Kun, Cai, Xusheng, Zhang, Ming, Zhang
Format: Book
Language:English
Published: Springer 2023
Subjects:
AE-Informer
model implements the AE
Online Access:https://link.springer.com/article/10.1186/s42834-023-00175-w
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045
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spelling oai:localhost:PNK-80452023-04-18T08:14:07Z Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model Kun, Cai Xusheng, Zhang Ming, Zhang AE-Informer model implements the AE CC BY 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 cannot provide effective information to prevent the negative effects of air pollution. Therefore, few existing methods could obtain accurate air pollutant time series predictions. To this end, a deep learning-based air pollutant prediction method, namely, the autocorrelation error-Informer (AE-Informer) model, is proposed in this study. The model implements the AE based on the Informer model. The AE-Informer model is used to predict the hourly concentrations of multiple air pollutants, including PM10, PM2.5, NO2, and O3. 2023-04-18T08:14:07Z 2023-04-18T08:14:07Z 2023 Book https://link.springer.com/article/10.1186/s42834-023-00175-w https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic AE-Informer
model implements the AE
spellingShingle AE-Informer
model implements the AE
Kun, Cai
Xusheng, Zhang
Ming, Zhang
Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
description CC BY
format Book
author Kun, Cai
Xusheng, Zhang
Ming, Zhang
author_facet Kun, Cai
Xusheng, Zhang
Ming, Zhang
author_sort Kun, Cai
title Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
title_short Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
title_full Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
title_fullStr Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
title_full_unstemmed Improving air pollutant prediction in Henan Province, China, by enhancing the concentration prediction accuracy using autocorrelation errors and an Informer deep learning model
title_sort improving air pollutant prediction in henan province, china, by enhancing the concentration prediction accuracy using autocorrelation errors and an informer deep learning model
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1186/s42834-023-00175-w
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8045
_version_ 1763543258269483008
score 8.887929

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