Data analysis and preprocessing techniques for air quality prediction: a survey
I tiakina i:
| I whakaputaina i: | Stochastic Environmental Research and Risk Assessment vol. 38, no. 6 (Jun 2024), p. 2095 |
|---|---|
| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , |
| I whakaputaina: |
Springer Nature B.V.
|
| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
|
| Whakarāpopotonga: | 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 |
| Puna: | Science Database |