Data analysis and preprocessing techniques for air quality prediction: a survey
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| הוצא לאור ב: | Stochastic Environmental Research and Risk Assessment vol. 38, no. 6 (Jun 2024), p. 2095 |
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| מחבר ראשי: | |
| מחברים אחרים: | , , |
| יצא לאור: |
Springer Nature B.V.
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| נושאים: | |
| גישה מקוונת: | Citation/Abstract Full Text - PDF |
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אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
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MARC
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| 022 | |a 1436-3240 | ||
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| 022 | |a 0931-1955 | ||
| 024 | 7 | |a 10.1007/s00477-024-02693-4 |2 doi | |
| 035 | |a 3059654952 | ||
| 045 | 2 | |b d20240601 |b d20240630 | |
| 084 | |a 65704 |2 nlm | ||
| 100 | 1 | |a Yu, Chengqing |u University of Chinese Academy of Sciences, Beijing, China (GRID:grid.410726.6) (ISNI:0000 0004 1797 8419) | |
| 245 | 1 | |a Data analysis and preprocessing techniques for air quality prediction: a survey | |
| 260 | |b Springer Nature B.V. |c Jun 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Data processing | ||
| 653 | |a Outliers (statistics) | ||
| 653 | |a Data analysis | ||
| 653 | |a Preprocessing | ||
| 653 | |a Spatial data | ||
| 653 | |a Interpolation | ||
| 653 | |a Air quality | ||
| 653 | |a Nonlinear systems | ||
| 653 | |a Decomposition | ||
| 653 | |a Pollutants | ||
| 653 | |a Forecasting | ||
| 653 | |a Environmental research | ||
| 653 | |a Feature selection | ||
| 653 | |a Air pollution | ||
| 653 | |a Outdoor air quality | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Risk assessment | ||
| 653 | |a Emergency communications systems | ||
| 653 | |a Missing data | ||
| 653 | |a Algorithms | ||
| 653 | |a Nitrogen dioxide | ||
| 653 | |a Environmental governance | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Tan, Jing |u Central South University, School of Traffic and Transportation Engineering, Changsha, China (GRID:grid.216417.7) (ISNI:0000 0001 0379 7164) | |
| 700 | 1 | |a Cheng, Yihan |u Beijing Jiaotong University, School of Traffic and Transportation, Beijing, China (GRID:grid.181531.f) (ISNI:0000 0004 1789 9622) | |
| 700 | 1 | |a Mi, Xiwei |u Beijing Jiaotong University, School of Traffic and Transportation, Beijing, China (GRID:grid.181531.f) (ISNI:0000 0004 1789 9622) | |
| 773 | 0 | |t Stochastic Environmental Research and Risk Assessment |g vol. 38, no. 6 (Jun 2024), p. 2095 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3059654952/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3059654952/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |