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...
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 |
Similar Items
-
Fault Prediction for Nonlinear System Using Sliding ARMA Combined with Online LS-SVR
by: Shengchao Su, et al.
Published: (2014-01-01) -
Nanjing Lectures (2016-2019)
by: Stiegler, Bernard
Published: (2020) -
Nanjing Lectures (2016-2019)
Published: (2020) -
Ventilation and Air Quality in Student Dormitories in China: A Case Study during Summer in Nanjing
by: Zhe Yang, et al.
Published: (2018-06-01) -
Air-sea interaction in the Arctic Ocean from measurements in the summer-autumn period
by: I.A. Repina, et al.
Published: (2019-12-01)