Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting
The impact of fine particulate matter on health has captured attention worldwide. Many studies have proven that fine particulate matter harms the respiratory system and the cardiovascular system. To prevent people from being harmed, many scientific research studies on PM<sub>2.5</sub> pr...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9493244/ |
| _version_ | 1818654784893222912 |
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| author | Pei-Wen Chiang Shi-Jinn Horng |
| author_facet | Pei-Wen Chiang Shi-Jinn Horng |
| author_sort | Pei-Wen Chiang |
| collection | DOAJ |
| description | The impact of fine particulate matter on health has captured attention worldwide. Many studies have proven that fine particulate matter harms the respiratory system and the cardiovascular system. To prevent people from being harmed, many scientific research studies on PM<sub>2.5</sub> prediction have been conducted in recent years. Accurate PM<sub>2.5</sub> forecasting can not only alert people to stay away from concentrated areas but also provide the government with environmental policies in the future. In this paper, we propose a hybrid time-series prediction framework for daily-based PM<sub>2.5</sub> forecasting. The proposed framework consists of three components: the autoencoder, the dilated convolutional neural network, and the gated recurrent unit. The experimental dataset with 76 monitoring stations from the Taiwan Environmental Protection Administration is applied for comparison of the baseline and the proposed models. The proposed model is not only for the specified city-/county-wide region but also for the particular monitoring station/site to predict PM<sub>2.5</sub> concentration. By considering air quality data, meteorological data, and geographical data simultaneously, the proposed model can increase the accuracy of PM<sub>2.5</sub> prediction. In addition, the proposed PM<sub>2.5</sub> forecasting model can learn the location-centric spatial features and the daily-based temporal features simultaneously. The experimental results show that the prediction accuracy of the proposed model is superior to those of the baseline models. |
| first_indexed | 2024-12-17T02:59:18Z |
| format | Article |
| id | doaj.art-ddbfd3301bc6492d8249bca9b0c2936c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T02:59:18Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ddbfd3301bc6492d8249bca9b0c2936c2022-12-21T22:06:09ZengIEEEIEEE Access2169-35362021-01-01910416210417610.1109/ACCESS.2021.30991119493244Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> ForecastingPei-Wen Chiang0https://orcid.org/0000-0002-2942-7345Shi-Jinn Horng1https://orcid.org/0000-0002-9978-0400Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, TaiwanThe impact of fine particulate matter on health has captured attention worldwide. Many studies have proven that fine particulate matter harms the respiratory system and the cardiovascular system. To prevent people from being harmed, many scientific research studies on PM<sub>2.5</sub> prediction have been conducted in recent years. Accurate PM<sub>2.5</sub> forecasting can not only alert people to stay away from concentrated areas but also provide the government with environmental policies in the future. In this paper, we propose a hybrid time-series prediction framework for daily-based PM<sub>2.5</sub> forecasting. The proposed framework consists of three components: the autoencoder, the dilated convolutional neural network, and the gated recurrent unit. The experimental dataset with 76 monitoring stations from the Taiwan Environmental Protection Administration is applied for comparison of the baseline and the proposed models. The proposed model is not only for the specified city-/county-wide region but also for the particular monitoring station/site to predict PM<sub>2.5</sub> concentration. By considering air quality data, meteorological data, and geographical data simultaneously, the proposed model can increase the accuracy of PM<sub>2.5</sub> prediction. In addition, the proposed PM<sub>2.5</sub> forecasting model can learn the location-centric spatial features and the daily-based temporal features simultaneously. The experimental results show that the prediction accuracy of the proposed model is superior to those of the baseline models.https://ieeexplore.ieee.org/document/9493244/Autoencoderdilated convolutional neural networkgated recurrent unitPM₂.₅ forecasting |
| spellingShingle | Pei-Wen Chiang Shi-Jinn Horng Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting IEEE Access Autoencoder dilated convolutional neural network gated recurrent unit PM₂.₅ forecasting |
| title | Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting |
| title_full | Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting |
| title_fullStr | Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting |
| title_full_unstemmed | Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting |
| title_short | Hybrid Time-Series Framework for Daily-Based PM<sub>2.5</sub> Forecasting |
| title_sort | hybrid time series framework for daily based pm sub 2 5 sub forecasting |
| topic | Autoencoder dilated convolutional neural network gated recurrent unit PM₂.₅ forecasting |
| url | https://ieeexplore.ieee.org/document/9493244/ |
| work_keys_str_mv | AT peiwenchiang hybridtimeseriesframeworkfordailybasedpmsub25subforecasting AT shijinnhorng hybridtimeseriesframeworkfordailybasedpmsub25subforecasting |