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Data analysis and preprocessing techniques for air quality prediction: a survey

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Veröffentlicht in:Stochastic Environmental Research and Risk Assessment vol. 38, no. 6 (Jun 2024), p. 2095
1. Verfasser: Yu, Chengqing
Weitere Verfasser: Tan, Jing, Cheng, Yihan, Mi, Xiwei
Veröffentlicht:
Springer Nature B.V.
Schlagworte:
Data processing
Outliers (statistics)
Data analysis
Preprocessing
Spatial data
Interpolation
Air quality
Nonlinear systems
Decomposition
Pollutants
Forecasting
Environmental research
Feature selection
Air pollution
Outdoor air quality
Cognition & reasoning
Risk assessment
Emergency communications systems
Missing data
Algorithms
Nitrogen dioxide
Environmental governance
Economic
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Abstract:Air quality prediction technology can provide effective technical means for environmental governance. In recent years, due to the strong nonlinearity of data, there has been extensive research on data analysis and preprocessing techniques. This paper aims to comprehensively summarize and analyze the methods used in air quality forecasting, specifically focusing on four categories: data decomposition, dimensionality reduction, data correction, and spatial interpolation. Each method's purpose, characteristics, improvements, and implementation details are described in detail. The evaluation of data preprocessing methods is based on popularity, accuracy improvements, time consumption, maturity, and implementation difficulty. Among the existing methods, data decomposition and feature selection are commonly used and well-developed. However, outlier detection and spatial interpolation have limited applications and require further research. Furthermore, this paper discusses current challenges in applying these methods and future development trends, providing a valuable reference for future research.
ISSN:1436-3240
1436-3259
0931-1955
DOI:10.1007/s00477-024-02693-4
Quelle:Science Database