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ORIGINAL RESEARCH
On Prediction of Air Pollution Using
Piecewise Affine Models
1
College of Computer Science and Technology & College of Data Science, Taiyuan University of Technology,
Shanxi, China
Submission date: 2024-01-22
Final revision date: 2024-02-12
Acceptance date: 2024-03-05
Online publication date: 2024-06-14
Publication date: 2025-01-02
Corresponding author
Zhenxing Ren
College of Computer Science and Technology & College of Data Science, Taiyuan University of Technology, Shanxi, China
College of Computer Science and Technology & College of Data Science, Taiyuan University of Technology, Shanxi, China
Pol. J. Environ. Stud. 2025;34(1):93-100
KEYWORDS
piecewise affine modelprediction of air pollutantsclustering-based identificationmodel structure selection
TOPICS
ABSTRACT
Since air pollution affects both public health and economic growth, the issue has received more
attention recently. Model-based early warning systems or pollution management tactics can be used
to assist in combating dangerous air pollutants if accurate prediction models are available. This paper
presents an approach to forecasting air contaminants using a piecewise affine model, which has
a high prediction power. To identify the piecewise affine model, this study adopts effective clustering
to identify the model. The proposed hierarchical clustering method improves the widely used BIRCH
(Balanced Iterative Reducing and Clustering using Hierarchies) by adding a refining step to handle
clusters with arbitrary geometries. Additionally, an optimization strategy like GA (Genetic Algorithm)
is used to jointly estimate the model order and parameters. Measurements of Shenyang’s air quality are
used to illustrate the proposed approach, and the outcomes reflect the method’s good prediction ability.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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