Articles | Volume 19, issue 20
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-19-12935-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Oct 2019
Research article |
| 18 Oct 2019
| 18 Oct 2019
Development of a daily PM10 and PM2.5 prediction system using a deep long short-term memory neural network model
Hyun S. Kim
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Inyoung Park
School of Electrical Engineering and Computer Science, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Chul H. Song
CORRESPONDING AUTHOR
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Kyunghwa Lee
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Jae W. Yun
School of Electrical Engineering and Computer Science, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Hong K. Kim
School of Electrical Engineering and Computer Science, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Moongu Jeon
School of Electrical Engineering and Computer Science, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Jiwon Lee
School of Electrical Engineering and Computer Science, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
Kyung M. Han
School of Earth Sciences and Environmental Engineering, Gwangju
Institute of Science and Technology (GIST), Gwangju 61005, South Korea
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- Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data J. Lee et al. 10.5194/gmd-15-3797-2022
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- Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models B. Lagesse et al. 10.1021/acs.est.0c02549
- Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA A. Ozbek et al. 10.1007/s12517-021-06982-y
- Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul J. Koo et al. 10.3390/atmos15111276
- An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning I. Yeo & Y. Choi 10.3390/su132111889
- Summer precipitation prediction in eastern China based on machine learning P. Fan et al. 10.1007/s00382-022-06464-1
- Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain) J. González-Enrique et al. 10.3390/s21051770
- Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors X. Xie et al. 10.1007/s10661-024-13005-2
- Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data P. Kow et al. 10.1016/j.envpol.2022.119348
- New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China S. Wang et al. 10.3390/ijerph19127186
- Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms B. Kim et al. 10.1016/j.apr.2022.101547
- An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea H. Hong et al. 10.3390/atmos13091462
- Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China M. Ren et al. 10.1007/s10661-021-09233-5
- Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ+ S. Shams et al. 10.1016/j.scitotenv.2024.174158
- Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods E. Kristiani et al. 10.3390/su14042068
- Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement H. Sun et al. 10.5194/gmd-15-8439-2022
- Deep Learning Implementation Using Long Short Term Memory Architecture for PM2.5 Concentration Prediction: a Review T. Istiana et al. 10.1088/1755-1315/1105/1/012026
- Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data Q. Guo et al. 10.1016/j.chemosphere.2023.139886
- An ensemble learning based hybrid model and framework for air pollution forecasting Y. Chang et al. 10.1007/s11356-020-09855-1
- A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea K. Qadeer et al. 10.3390/app10113984
- Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives K. Mehmood et al. 10.1016/j.jclepro.2022.134656
- Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
- Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea D. Singh et al. 10.1016/j.atmosres.2024.107283
- A Development of PM2.5 Forecasting System in South Korea Using Chemical Transport Modeling and Machine Learning Y. Koo et al. 10.1007/s13143-023-00314-8
- Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
- Prediction of hourly PM10 concentration through a hybrid deep learning-based method S. Nasabpour Molaei et al. 10.1007/s12145-023-01146-w
- Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration P. Fung et al. 10.1016/j.jaerosci.2020.105694
- Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port H. Hong et al. 10.3390/atmos12091172
- NUMAC: Description of the Nested Unified Model With Aerosols and Chemistry, and Evaluation With KORUS‐AQ Data H. Gordon et al. 10.1029/2022MS003457
- Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India C. Pande et al. 10.1007/s10661-024-13351-1
- Development of a PM2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea H. Chang-Hoi et al. 10.1016/j.atmosenv.2020.118021
- Development of a CNN+LSTM Hybrid Neural Network for Daily PM2.5 Prediction H. Kim et al. 10.3390/atmos13122124
- Modeling fine-grained spatio-temporal pollution maps with low-cost sensors S. Iyer et al. 10.1038/s41612-022-00293-z
- CoAN: A system framework correlating the air and noise pollution sensor data B. Maity et al. 10.1016/j.pmcj.2022.101546
- Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms I. Yeo et al. 10.1007/s00521-021-06082-8
- Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors M. Zaidan et al. 10.1109/JSEN.2020.3010316
- Performing indoor PM2.5prediction with low-cost data and machine learning B. Lagesse et al. 10.1108/F-05-2021-0046
- Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM L. Miao et al. 10.1016/j.atmosenv.2022.119362
- Machine learning based quantification of VOC contribution in surface ozone prediction R. Kalbande et al. 10.1016/j.chemosphere.2023.138474
- Evaluating traditional versus ensemble machine learning methods for predicting missing data of daily PM10 concentration E. Kalantari et al. 10.1016/j.apr.2024.102063
- Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data S. Jeong et al. 10.3390/s21030941
- Developing a wavelet-AI hybrid model for short- and long-term predictions of the pollutant concentration of particulate matter10 S. Mirzadeh et al. 10.1007/s13762-020-03123-y
45 citations as recorded by crossref.
- Machine learning and deep learning‐driven methods for predicting ambient particulate matters levels: A case study A. Wu et al. 10.1002/cpe.7035
- Development of a deep neural network for predicting 6 h average PM2.5 concentrations up to 2 subsequent days using various training data J. Lee et al. 10.5194/gmd-15-3797-2022
- Monitoring and prediction of dust concentration in an open-pit mine using a deep-learning algorithm L. Li et al. 10.1007/s40201-021-00613-0
- Application of aggregation operators for forecastingPM10fluctuations: From available Caribbean data sites to unequipped ones T. Plocoste et al. 10.1016/j.apr.2024.102116
- Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models B. Lagesse et al. 10.1021/acs.est.0c02549
- Prediction of 10-min, hourly, and daily atmospheric air temperature: comparison of LSTM, ANFIS-FCM, and ARMA A. Ozbek et al. 10.1007/s12517-021-06982-y
- Development of PM2.5 Forecast Model Combining ConvLSTM and DNN in Seoul J. Koo et al. 10.3390/atmos15111276
- An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning I. Yeo & Y. Choi 10.3390/su132111889
- Summer precipitation prediction in eastern China based on machine learning P. Fan et al. 10.1007/s00382-022-06464-1
- Artificial Neural Networks, Sequence-to-Sequence LSTMs, and Exogenous Variables as Analytical Tools for NO2 (Air Pollution) Forecasting: A Case Study in the Bay of Algeciras (Spain) J. González-Enrique et al. 10.3390/s21051770
- Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors X. Xie et al. 10.1007/s10661-024-13005-2
- Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data P. Kow et al. 10.1016/j.envpol.2022.119348
- New Deep Learning Model to Estimate Ozone Concentrations Found Worrying Exposure Level over Eastern China S. Wang et al. 10.3390/ijerph19127186
- Short-term prediction of particulate matter (PM10 and PM2.5) in Seoul, South Korea using tree-based machine learning algorithms B. Kim et al. 10.1016/j.apr.2022.101547
- An Air Pollutants Prediction Method Integrating Numerical Models and Artificial Intelligence Models Targeting the Area around Busan Port in Korea H. Hong et al. 10.3390/atmos13091462
- Combining machine learning models through multiple data division methods for PM2.5 forecasting in Northern Xinjiang, China M. Ren et al. 10.1007/s10661-021-09233-5
- Innovative approaches for accurate ozone prediction and health risk analysis in South Korea: The combined effectiveness of deep learning and AirQ+ S. Shams et al. 10.1016/j.scitotenv.2024.174158
- Short-Term Prediction of PM2.5 Using LSTM Deep Learning Methods E. Kristiani et al. 10.3390/su14042068
- Development of an LSTM broadcasting deep-learning framework for regional air pollution forecast improvement H. Sun et al. 10.5194/gmd-15-8439-2022
- Deep Learning Implementation Using Long Short Term Memory Architecture for PM2.5 Concentration Prediction: a Review T. Istiana et al. 10.1088/1755-1315/1105/1/012026
- Simulating daily PM2.5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data Q. Guo et al. 10.1016/j.chemosphere.2023.139886
- An ensemble learning based hybrid model and framework for air pollution forecasting Y. Chang et al. 10.1007/s11356-020-09855-1
- A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea K. Qadeer et al. 10.3390/app10113984
- Predicting the quality of air with machine learning approaches: Current research priorities and future perspectives K. Mehmood et al. 10.1016/j.jclepro.2022.134656
- Analyzing and Improving the Performance of a Particulate Matter Low Cost Air Quality Monitoring Device E. Bagkis et al. 10.3390/atmos12020251
- Deep-BCSI: A deep learning-based framework for bias correction and spatial imputation of PM2.5 concentrations in South Korea D. Singh et al. 10.1016/j.atmosres.2024.107283
- A Development of PM2.5 Forecasting System in South Korea Using Chemical Transport Modeling and Machine Learning Y. Koo et al. 10.1007/s13143-023-00314-8
- Development of Korean Air Quality Prediction System version 1 (KAQPS v1) with focuses on practical issues K. Lee et al. 10.5194/gmd-13-1055-2020
- Prediction of hourly PM10 concentration through a hybrid deep learning-based method S. Nasabpour Molaei et al. 10.1007/s12145-023-01146-w
- Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration P. Fung et al. 10.1016/j.jaerosci.2020.105694
- Incorporation of Shipping Activity Data in Recurrent Neural Networks and Long Short-Term Memory Models to Improve Air Quality Predictions around Busan Port H. Hong et al. 10.3390/atmos12091172
- NUMAC: Description of the Nested Unified Model With Aerosols and Chemistry, and Evaluation With KORUS‐AQ Data H. Gordon et al. 10.1029/2022MS003457
- Evaluation of machine learning and deep learning models for daily air quality index prediction in Delhi city, India C. Pande et al. 10.1007/s10661-024-13351-1
- Development of a PM2.5 prediction model using a recurrent neural network algorithm for the Seoul metropolitan area, Republic of Korea H. Chang-Hoi et al. 10.1016/j.atmosenv.2020.118021
- Development of a CNN+LSTM Hybrid Neural Network for Daily PM2.5 Prediction H. Kim et al. 10.3390/atmos13122124
- Modeling fine-grained spatio-temporal pollution maps with low-cost sensors S. Iyer et al. 10.1038/s41612-022-00293-z
- CoAN: A system framework correlating the air and noise pollution sensor data B. Maity et al. 10.1016/j.pmcj.2022.101546
- Efficient PM2.5 forecasting using geographical correlation based on integrated deep learning algorithms I. Yeo et al. 10.1007/s00521-021-06082-8
- Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors M. Zaidan et al. 10.1109/JSEN.2020.3010316
- Performing indoor PM2.5prediction with low-cost data and machine learning B. Lagesse et al. 10.1108/F-05-2021-0046
- Estimation of daily ground-level PM2.5 concentrations over the Pearl River Delta using 1 km resolution MODIS AOD based on multi-feature BiLSTM L. Miao et al. 10.1016/j.atmosenv.2022.119362
- Machine learning based quantification of VOC contribution in surface ozone prediction R. Kalbande et al. 10.1016/j.chemosphere.2023.138474
- Evaluating traditional versus ensemble machine learning methods for predicting missing data of daily PM10 concentration E. Kalantari et al. 10.1016/j.apr.2024.102063
- Temperature Prediction Based on Bidirectional Long Short-Term Memory and Convolutional Neural Network Combining Observed and Numerical Forecast Data S. Jeong et al. 10.3390/s21030941
- Developing a wavelet-AI hybrid model for short- and long-term predictions of the pollutant concentration of particulate matter10 S. Mirzadeh et al. 10.1007/s13762-020-03123-y
Latest update: 10 Dec 2024
Short summary
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM) model was developed for daily PM10 and PM2.5 predictions in South Korea. In general, the accuracies of the LSTM-based predictions were superior to the 3-D CTM-based predictions. Based on this, we concluded that the LSTM-based system could be applied to daily operational PM forecasts in South Korea. We expect that similar AI systems can be applied to the predictions of other atmospheric pollutants.
In this study, a deep recurrent neural network system based on a long short-term memory (LSTM)...
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