Integration of complete ensemble empirical mode decomposition with deep long short-term memory model for particulate matter concentration prediction
The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data serie...
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| 主要な著者: | , , , , , , , , |
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| フォーマット: | 論文 |
| 出版事項: |
Springer Science and Business Media Deutschland GmbH
2021
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| 主題: | |
| オンライン･アクセス: | http://eprints.utm.my/id/eprint/94129/ http://dx.doi.org/10.1007/s11356-021-15574-y |
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