An ensembled method for air quality monitoring and control using machine learning
Air quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Measurement: Sensors |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665917423002507 |
| _version_ | 1797454290750537728 |
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| author | S John Livingston S. Deepa Kanmani A. Shamila Ebenezer Dahlia Sam A. Joshi |
| author_facet | S John Livingston S. Deepa Kanmani A. Shamila Ebenezer Dahlia Sam A. Joshi |
| author_sort | S John Livingston |
| collection | DOAJ |
| description | Air quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution control measures to diminish the effect of air contamination. Air Quality Index (AQI) helps in understanding the level of pollution. This study briefs various state of art methods such as SVM, RF,ANN, RNN and FL in predicting air quality using machine learning. The results shows prominent outcomes which can be deployed in various cost effective hardware platforms for household and commercial purposes. The proposed model used eight parameters like NO2CO, O3, PM2.5, PM10, SO2, TEMP, PRES, DEWP, RAIN,WD, WSPM of Beijing dataset in order to predict AQI and it is tested with regional dataset. The Proposed model works based on process monitoring model. The efficient and flexible way to solve environmental issues is to combine machine learning algorithm with air quality prediction. |
| first_indexed | 2024-03-09T15:36:08Z |
| format | Article |
| id | doaj.art-860547a94b6946c997fe2a61a9a47bc0 |
| institution | Directory Open Access Journal |
| issn | 2665-9174 |
| language | English |
| last_indexed | 2024-03-09T15:36:08Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Measurement: Sensors |
| spelling | doaj.art-860547a94b6946c997fe2a61a9a47bc02023-11-26T05:13:49ZengElsevierMeasurement: Sensors2665-91742023-12-0130100914An ensembled method for air quality monitoring and control using machine learningS John Livingston0S. Deepa Kanmani1A. Shamila Ebenezer2Dahlia Sam3A. Joshi4The American College, Madurai, IndiaDepartment of Information Technology, Sri Krishna College of Engineering and Technology, Coimbatore, IndiaDepartment of Computer Science and Engineering Karunya University, Coimbatore, IndiaDepartment of Information Technology, Dr.M.G.R. Educational and Research Institute, India; Corresponding author.Department of AI &DS, Panimalar Engineering College, Chennai, IndiaAir quality monitoring is a significant job in our everyday life and affects the vitality of a human. It paves a better way of understanding the sources, effects and levels of pollutants in the air inhaled by the population of the region. This concept helps us in improving and developing pollution control measures to diminish the effect of air contamination. Air Quality Index (AQI) helps in understanding the level of pollution. This study briefs various state of art methods such as SVM, RF,ANN, RNN and FL in predicting air quality using machine learning. The results shows prominent outcomes which can be deployed in various cost effective hardware platforms for household and commercial purposes. The proposed model used eight parameters like NO2CO, O3, PM2.5, PM10, SO2, TEMP, PRES, DEWP, RAIN,WD, WSPM of Beijing dataset in order to predict AQI and it is tested with regional dataset. The Proposed model works based on process monitoring model. The efficient and flexible way to solve environmental issues is to combine machine learning algorithm with air quality prediction.http://www.sciencedirect.com/science/article/pii/S2665917423002507Air pollutionAir quality indexAir pollutantAmbient air quality |
| spellingShingle | S John Livingston S. Deepa Kanmani A. Shamila Ebenezer Dahlia Sam A. Joshi An ensembled method for air quality monitoring and control using machine learning Measurement: Sensors Air pollution Air quality index Air pollutant Ambient air quality |
| title | An ensembled method for air quality monitoring and control using machine learning |
| title_full | An ensembled method for air quality monitoring and control using machine learning |
| title_fullStr | An ensembled method for air quality monitoring and control using machine learning |
| title_full_unstemmed | An ensembled method for air quality monitoring and control using machine learning |
| title_short | An ensembled method for air quality monitoring and control using machine learning |
| title_sort | ensembled method for air quality monitoring and control using machine learning |
| topic | Air pollution Air quality index Air pollutant Ambient air quality |
| url | http://www.sciencedirect.com/science/article/pii/S2665917423002507 |
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