Deep learning based multimodal urban air quality prediction and traffic analytics
<p dir="ltr">Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and man...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2023
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| _version_ | 1864513507686350848 |
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| author | Saad Hameed (6488738) |
| author2 | Ashadul Islam (19438027) Kashif Ahmad (12592762) Samir Brahim Belhaouari (9427347) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| author2_role | author author author author author |
| author_facet | Saad Hameed (6488738) Ashadul Islam (19438027) Kashif Ahmad (12592762) Samir Brahim Belhaouari (9427347) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| author_role | author |
| dc.creator.none.fl_str_mv | Saad Hameed (6488738) Ashadul Islam (19438027) Kashif Ahmad (12592762) Samir Brahim Belhaouari (9427347) Junaid Qadir (16494902) Ala Al-Fuqaha (4434340) |
| dc.date.none.fl_str_mv | 2023-12-13T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-023-49296-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Deep_learning_based_multimodal_urban_air_quality_prediction_and_traffic_analytics/26772190 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Environmental sciences Environmental management Health sciences Public health Information and computing sciences Artificial intelligence Machine learning Air Quality Prediction Environmental Pollution Vehicle Traffic Public Health Artificial Intelligence (AI) Sensor Technology Multi-modal Framework |
| dc.title.none.fl_str_mv | Deep learning based multimodal urban air quality prediction and traffic analytics |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-49296-7" target="_blank">https://dx.doi.org/10.1038/s41598-023-49296-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_70875994d0f13bbf63a86b2bfd0930f8 |
| identifier_str_mv | 10.1038/s41598-023-49296-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26772190 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Deep learning based multimodal urban air quality prediction and traffic analyticsSaad Hameed (6488738)Ashadul Islam (19438027)Kashif Ahmad (12592762)Samir Brahim Belhaouari (9427347)Junaid Qadir (16494902)Ala Al-Fuqaha (4434340)Environmental sciencesEnvironmental managementHealth sciencesPublic healthInformation and computing sciencesArtificial intelligenceMachine learningAir Quality PredictionEnvironmental PollutionVehicle TrafficPublic HealthArtificial Intelligence (AI)Sensor TechnologyMulti-modal Framework<p dir="ltr">Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-023-49296-7" target="_blank">https://dx.doi.org/10.1038/s41598-023-49296-7</a></p>2023-12-13T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-023-49296-7https://figshare.com/articles/journal_contribution/Deep_learning_based_multimodal_urban_air_quality_prediction_and_traffic_analytics/26772190CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267721902023-12-13T09:00:00Z |
| spellingShingle | Deep learning based multimodal urban air quality prediction and traffic analytics Saad Hameed (6488738) Environmental sciences Environmental management Health sciences Public health Information and computing sciences Artificial intelligence Machine learning Air Quality Prediction Environmental Pollution Vehicle Traffic Public Health Artificial Intelligence (AI) Sensor Technology Multi-modal Framework |
| status_str | publishedVersion |
| title | Deep learning based multimodal urban air quality prediction and traffic analytics |
| title_full | Deep learning based multimodal urban air quality prediction and traffic analytics |
| title_fullStr | Deep learning based multimodal urban air quality prediction and traffic analytics |
| title_full_unstemmed | Deep learning based multimodal urban air quality prediction and traffic analytics |
| title_short | Deep learning based multimodal urban air quality prediction and traffic analytics |
| title_sort | Deep learning based multimodal urban air quality prediction and traffic analytics |
| topic | Environmental sciences Environmental management Health sciences Public health Information and computing sciences Artificial intelligence Machine learning Air Quality Prediction Environmental Pollution Vehicle Traffic Public Health Artificial Intelligence (AI) Sensor Technology Multi-modal Framework |