Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model

Abstract In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy...

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Main Authors: Bing Liu, Yueqiang Jin, Chaoyang Li
Format: Article
Language:English
Published: Nature Publishing Group 2021-01-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-020-79462-0
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spelling doaj-433af00dc7094b7ea2e32adf303b122d2021-01-17T12:43:23ZengNature Publishing GroupScientific Reports2045-23222021-01-0111111410.1038/s41598-020-79462-0Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined modelBing Liu0Yueqiang Jin1Chaoyang Li2Public Foundational Courses Department, Nanjing Vocational University of Industry TechnologyPublic Foundational Courses Department, Nanjing Vocational University of Industry TechnologyCollege of Management, Henan University of TechnologyAbstract In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.https://doi.org/10.1038/s41598-020-79462-0
collection DOAJ
language English
format Article
sources DOAJ
author Bing Liu
Yueqiang Jin
Chaoyang Li
spellingShingle Bing Liu
Yueqiang Jin
Chaoyang Li
Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
Scientific Reports
author_facet Bing Liu
Yueqiang Jin
Chaoyang Li
author_sort Bing Liu
title Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
title_short Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
title_full Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
title_fullStr Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
title_full_unstemmed Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model
title_sort analysis and prediction of air quality in nanjing from autumn 2018 to summer 2019 using pcr–svr–arma combined model
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-01-01
description Abstract In order to correct the monitoring data of the miniature air quality detector, an air quality prediction model fusing Principal Component Regression (PCR), Support Vector Regression (SVR) machine, and Autoregressive Moving Average (ARMA) model was proposed to improve the prediction accuracy of the six types of pollutants in the air. First, the main information of factors affecting air quality is extracted by principal component analysis, and then principal component regression is used to give the predicted values of six types of pollutants. Second, the support vector regression machine is used to regress the predicted value of principal component regression and various influencing factors. Finally, the autoregressive moving average model is used to correct the residual items, and finally the predicted values of six types of pollutants are obtained. The experimental results showed that the proposed combination prediction model of PCR–SVR–ARMA had a better prediction effect than the artificial neural network, the standard support vector regression machine, the principal component regression, and PCR–SVR method. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and relative Mean Absolute Percent Error (MAPE) are used as evaluation indicators to evaluate the PCR–SVR–ARMA model. This model can increase the accuracy of self-built points by 72.6% to 93.2%, and the model has excellent prediction effects in the training set and detection set, indicating that the model has good generalization ability. This model can play an active role scientific arrangement and promotion of miniature air quality detectors and grid-based monitoring of the concentration of various pollutants.
url https://doi.org/10.1038/s41598-020-79462-0
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AT chaoyangli analysisandpredictionofairqualityinnanjingfromautumn2018tosummer2019usingpcrsvrarmacombinedmodel
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