A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance
Abstract Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available...
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Nature Portfolio
2021
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oai:doaj.org-article:61a2b4505f114dcaba24ee227ece13772021-12-02T14:47:31ZA novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance10.1038/s41598-021-90446-62045-2322https://doaj.org/article/61a2b4505f114dcaba24ee227ece13772021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90446-6https://doaj.org/toc/2045-2322Abstract Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.Alqamah SayeedYunsoo ChoiEbrahim EslamiJia JungYannic LopsAhmed Khan SalmanJae-Bum LeeHyun-Ju ParkMin-Hyeok ChoiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q Alqamah Sayeed Yunsoo Choi Ebrahim Eslami Jia Jung Yannic Lops Ahmed Khan Salman Jae-Bum Lee Hyun-Ju Park Min-Hyeok Choi A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| description |
Abstract Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants. |
| format |
article |
| author |
Alqamah Sayeed Yunsoo Choi Ebrahim Eslami Jia Jung Yannic Lops Ahmed Khan Salman Jae-Bum Lee Hyun-Ju Park Min-Hyeok Choi |
| author_facet |
Alqamah Sayeed Yunsoo Choi Ebrahim Eslami Jia Jung Yannic Lops Ahmed Khan Salman Jae-Bum Lee Hyun-Ju Park Min-Hyeok Choi |
| author_sort |
Alqamah Sayeed |
| title |
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| title_short |
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| title_full |
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| title_fullStr |
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| title_full_unstemmed |
A novel CMAQ-CNN hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| title_sort |
novel cmaq-cnn hybrid model to forecast hourly surface-ozone concentrations 14 days in advance |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/61a2b4505f114dcaba24ee227ece1377 |
| work_keys_str_mv |
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