Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU
Air pollution forecasting can provide reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8732985/ |
| _version_ | 1818331793549426688 |
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| author | Qing Tao Fang Liu Yong Li Denis Sidorov |
| author_facet | Qing Tao Fang Liu Yong Li Denis Sidorov |
| author_sort | Qing Tao |
| collection | DOAJ |
| description | Air pollution forecasting can provide reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to $2.5~\mu \text{m}$ ) concentration, and the convolutional-based bidirectional gated recurrent unit (CBGRU) method is presented, which combines 1D convnets (convolutional neural networks) and bidirectional GRU (gated recurrent unit) neural networks. The case is carried out by using the Beijing PM2.5 data set in UCI Machine Learning Repository. Comparing the prediction results with the traditional ones, it is proved that the error of the CBGRU model is lower and the prediction performance is better. |
| first_indexed | 2024-12-13T13:25:30Z |
| format | Article |
| id | doaj.art-b1ad93dcfd3245848b06a557c1fdcf37 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:25:30Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-b1ad93dcfd3245848b06a557c1fdcf372022-12-21T23:44:18ZengIEEEIEEE Access2169-35362019-01-017766907669810.1109/ACCESS.2019.29215788732985Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRUQing Tao0Fang Liu1https://orcid.org/0000-0003-0750-8344Yong Li2https://orcid.org/0000-0002-1183-5359Denis Sidorov3https://orcid.org/0000-0002-3131-1325School of Automation, Central South University, Changsha, ChinaSchool of Automation, Central South University, Changsha, ChinaCollege of Electrical and Information Engineering, Hunan University, Changsha, China130 Lermontov Str., Energy Systems Institute of Russian Academy of Sciences, Irkutsk, RussiaAir pollution forecasting can provide reliable information about the future pollution situation, which is useful for an efficient operation of air pollution control and helps to plan for prevention. Dynamics of air pollution are usually reflected by various factors, such as the temperature, humidity, wind direction, wind speed, snowfall, rainfall, and so on, which increase the difficulty in understanding the change of air pollutant concentration. In this paper, a short-term forecasting model based on deep learning is proposed for PM2.5 (particulate matter with an aerodynamic diameter less than or equal to $2.5~\mu \text{m}$ ) concentration, and the convolutional-based bidirectional gated recurrent unit (CBGRU) method is presented, which combines 1D convnets (convolutional neural networks) and bidirectional GRU (gated recurrent unit) neural networks. The case is carried out by using the Beijing PM2.5 data set in UCI Machine Learning Repository. Comparing the prediction results with the traditional ones, it is proved that the error of the CBGRU model is lower and the prediction performance is better.https://ieeexplore.ieee.org/document/8732985/Air pollution forecastingdeep learning1D convolutional neural networksbidirectional gated recurrent unit |
| spellingShingle | Qing Tao Fang Liu Yong Li Denis Sidorov Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU IEEE Access Air pollution forecasting deep learning 1D convolutional neural networks bidirectional gated recurrent unit |
| title | Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU |
| title_full | Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU |
| title_fullStr | Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU |
| title_full_unstemmed | Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU |
| title_short | Air Pollution Forecasting Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU |
| title_sort | air pollution forecasting using a deep learning model based on 1d convnets and bidirectional gru |
| topic | Air pollution forecasting deep learning 1D convolutional neural networks bidirectional gated recurrent unit |
| url | https://ieeexplore.ieee.org/document/8732985/ |
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