Air Quality Index Prediction Using DNN-Markov Modeling
Air quality measurements contribute to diverse socio-economic sectors, including the environment and healthcare. Many methods are commonly applied to present air-quality levels, reflecting differing national standards. This study presents an air quality index prediction model, to measure air polluti...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | https://www.tandfonline.com/doi/10.1080/08839514.2024.2371540 |
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| author | Roba Zayed Maysam Abbod |
| author_facet | Roba Zayed Maysam Abbod |
| author_sort | Roba Zayed |
| collection | DOAJ |
| description | Air quality measurements contribute to diverse socio-economic sectors, including the environment and healthcare. Many methods are commonly applied to present air-quality levels, reflecting differing national standards. This study presents an air quality index prediction model, to measure air pollution levels for healthcare applications in congested areas. DNN-Markov modeling techniques are used to predict air quality, based on environmental conditions at peak hours. The developed model presents different approaches for highly accurate prediction of the air quality index for the next hour at a given location, under specific environmental conditions. This system could be used to support planning decisions related to the consequences of air quality. The study was conducted in selected locations in Jordan and England as a comparative model prediction accuracy study using different big-data sets of multivariate time series in traffic-heavy locations. The air quality index was represented using Neuro Fuzzy Logic as a method to contribute in air quality index predictions within blurry (boundary) values. The selected DNN-Markov hybrid model could predict air quality with accuracy of around (RMSE 7.86) for the location in England, and around (RMSE 15.27) for the one in Jordan. |
| format | Article |
| id | doaj-art-4e9df10686444ced8864aea6429ffbd0 |
| institution | OA Journals |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj-art-4e9df10686444ced8864aea6429ffbd02025-08-20T01:56:56ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452024-12-0138110.1080/08839514.2024.2371540Air Quality Index Prediction Using DNN-Markov ModelingRoba Zayed0Maysam Abbod1Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge, UKDepartment of Electronic and Electrical Engineering, Brunel University London, Uxbridge, UKAir quality measurements contribute to diverse socio-economic sectors, including the environment and healthcare. Many methods are commonly applied to present air-quality levels, reflecting differing national standards. This study presents an air quality index prediction model, to measure air pollution levels for healthcare applications in congested areas. DNN-Markov modeling techniques are used to predict air quality, based on environmental conditions at peak hours. The developed model presents different approaches for highly accurate prediction of the air quality index for the next hour at a given location, under specific environmental conditions. This system could be used to support planning decisions related to the consequences of air quality. The study was conducted in selected locations in Jordan and England as a comparative model prediction accuracy study using different big-data sets of multivariate time series in traffic-heavy locations. The air quality index was represented using Neuro Fuzzy Logic as a method to contribute in air quality index predictions within blurry (boundary) values. The selected DNN-Markov hybrid model could predict air quality with accuracy of around (RMSE 7.86) for the location in England, and around (RMSE 15.27) for the one in Jordan.https://www.tandfonline.com/doi/10.1080/08839514.2024.2371540 |
| spellingShingle | Roba Zayed Maysam Abbod Air Quality Index Prediction Using DNN-Markov Modeling Applied Artificial Intelligence |
| title | Air Quality Index Prediction Using DNN-Markov Modeling |
| title_full | Air Quality Index Prediction Using DNN-Markov Modeling |
| title_fullStr | Air Quality Index Prediction Using DNN-Markov Modeling |
| title_full_unstemmed | Air Quality Index Prediction Using DNN-Markov Modeling |
| title_short | Air Quality Index Prediction Using DNN-Markov Modeling |
| title_sort | air quality index prediction using dnn markov modeling |
| url | https://www.tandfonline.com/doi/10.1080/08839514.2024.2371540 |
| work_keys_str_mv | AT robazayed airqualityindexpredictionusingdnnmarkovmodeling AT maysamabbod airqualityindexpredictionusingdnnmarkovmodeling |