Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model

Haoyuan Zhang; Yilun Jin; Jiaxuan Shi; Shuai Zhang

doi:doi:10.11648/j.acm.20211006.14

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Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model

Haoyuan Zhang, Yilun Jin, Jiaxuan Shi, Shuai Zhang

Published in Applied and Computational Mathematics (Volume 10, Issue 6)

Received: 31 July 2021 Accepted: 22 November 2021 Published: 2 December 2021

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Abstract

With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.

Published in	Applied and Computational Mathematics (Volume 10, Issue 6)
DOI	10.11648/j.acm.20211006.14
Page(s)	156-162
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Ensemble Model, Stacking, Bayesian Optimization, PM2.5 Concentrations, Prediction, Air Quality

References

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APA Style

Haoyuan Zhang, Yilun Jin, Jiaxuan Shi, Shuai Zhang. (2021). Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Applied and Computational Mathematics, 10(6), 156-162. https://doi.org/10.11648/j.acm.20211006.14

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ACS Style

Haoyuan Zhang; Yilun Jin; Jiaxuan Shi; Shuai Zhang. Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Appl. Comput. Math. 2021, 10(6), 156-162. doi: 10.11648/j.acm.20211006.14

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AMA Style

Haoyuan Zhang, Yilun Jin, Jiaxuan Shi, Shuai Zhang. Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Appl Comput Math. 2021;10(6):156-162. doi: 10.11648/j.acm.20211006.14

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@article{10.11648/j.acm.20211006.14,
  author = {Haoyuan Zhang and Yilun Jin and Jiaxuan Shi and Shuai Zhang},
  title = {Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model},
  journal = {Applied and Computational Mathematics},
  volume = {10},
  number = {6},
  pages = {156-162},
  doi = {10.11648/j.acm.20211006.14},
  url = {https://doi.org/10.11648/j.acm.20211006.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20211006.14},
  abstract = {With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.},
 year = {2021}
}

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TY  - JOUR
T1  - Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model
AU  - Haoyuan Zhang
AU  - Yilun Jin
AU  - Jiaxuan Shi
AU  - Shuai Zhang
Y1  - 2021/12/02
PY  - 2021
N1  - https://doi.org/10.11648/j.acm.20211006.14
DO  - 10.11648/j.acm.20211006.14
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 156
EP  - 162
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20211006.14
AB  - With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.
VL  - 10
IS  - 6
ER  -

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Author Information

Haoyuan Zhang

Guanghua Cambridge International School, Shanghai, China
Yilun Jin

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Jiaxuan Shi

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China
Shuai Zhang

School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Haoyuan Zhang, Yilun Jin, Jiaxuan Shi, Shuai Zhang. (2021). Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Applied and Computational Mathematics, 10(6), 156-162. https://doi.org/10.11648/j.acm.20211006.14

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ACS Style

Haoyuan Zhang; Yilun Jin; Jiaxuan Shi; Shuai Zhang. Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Appl. Comput. Math. 2021, 10(6), 156-162. doi: 10.11648/j.acm.20211006.14

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AMA Style

Haoyuan Zhang, Yilun Jin, Jiaxuan Shi, Shuai Zhang. Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model. Appl Comput Math. 2021;10(6):156-162. doi: 10.11648/j.acm.20211006.14

Copy | Download

@article{10.11648/j.acm.20211006.14,
  author = {Haoyuan Zhang and Yilun Jin and Jiaxuan Shi and Shuai Zhang},
  title = {Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model},
  journal = {Applied and Computational Mathematics},
  volume = {10},
  number = {6},
  pages = {156-162},
  doi = {10.11648/j.acm.20211006.14},
  url = {https://doi.org/10.11648/j.acm.20211006.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20211006.14},
  abstract = {With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Predicting PM2.5 Concentrations Using Stacking-based Ensemble Model
AU  - Haoyuan Zhang
AU  - Yilun Jin
AU  - Jiaxuan Shi
AU  - Shuai Zhang
Y1  - 2021/12/02
PY  - 2021
N1  - https://doi.org/10.11648/j.acm.20211006.14
DO  - 10.11648/j.acm.20211006.14
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 156
EP  - 162
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20211006.14
AB  - With the increasingly serious air pollution problem, PM2.5 concentration, as an effective indicator to evaluate air quality, has attracted extensive attention from all sectors of society. Accurate prediction of PM2.5 concentrations is of great significance in providing the public with early air pollution warning information to protect public health. With a decade of development, artificial intelligence technology has given birth to various prediction models with high-performance, in particular, brought new impetus to the prediction of PM2.5 concentrations. In this study, a stacking-based ensemble model with self-adaptive hyper-parameter optimization is proposed to solve the PM2.5 concentrations prediction problem. First, the raw data are preprocessed with the normalization method to reduce the influence of the different orders of magnitude of input variables on model performance. Second, the Bayesian optimization method is used to optimize the hyper-parameters of the base predictors to improve their performance. Finally, a stacking ensemble method is applied to integrate the optimized base predictors into an ensemble model for final prediction. In the experiments, two datasets from the air quality stations in different areas are tested with four metrics to evaluate the performance of the proposed model in PM2.5 concentration prediction. The experimental results show that the proposed model outperforms other baseline models in solving the PM2.5 concentrations prediction problem.
VL  - 10
IS  - 6
ER  -

Copy | Download