Recurrent Learning on PM2.5 Prediction Based on Clustered Airbox Dataset
| Autor: | Ling-Jyh Chen, Wen Hsing Huang, Min-Te Sun, Wei-Shinn Ku, Ming Feng Ho, Kazuya Sakai, Chia Yu Lo |
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| Rok vydání: | 2022 |
| Předmět: |
Artificial neural network
Correlation coefficient Computer science Air pollution 02 engineering and technology medicine.disease_cause Autoencoder Computer Science Applications Airbox Computational Theory and Mathematics 020204 information systems Statistics 0202 electrical engineering electronic engineering information engineering medicine Cluster analysis Air quality index Predictive modelling Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 34:4994-5008 |
| ISSN: | 2326-3865 1041-4347 |
| DOI: | 10.1109/tkde.2020.3047634 |
| Popis: | The reliance on thermal power plants as well as increased vehicle emissions have constituted the primary factors of serious air pollution. Inhaling too much particulate air pollution may lead to respiratory diseases and even death, especially PM2.5. By predicting the air pollutant concentration, people can take precautions to avoid overexposure to air pollutants. Consequently, accurate PM2.5 prediction becomes more important. In this thesis, we propose a PM2.5 prediction system, which utilizes the dataset from EdiGreen Airbox and Taiwan EPA. Autoencoder and Linear interpolation are adopted for solving the missing value problem. Spearman's correlation coefficient is used to identify the most relevant features for PM2.5. Two prediction models (i.e., LSTM and LSTM based on K-means) are implemented which predict PM2.5 value for each Airbox device. To assess the performance of the model prediction, the daily average error and the hourly average accuracy for the duration of a week are calculated. The experimental results show that LSTM based on K-means has the best performance among all methods. |
| Databáze: | OpenAIRE |
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