Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques
Air pollution is a serious concern for public health, linked to many diseases and an increase in fatalities. To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. This study explores how supervised machine learning method...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/32/e3sconf_joe52025_02012.pdf |
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| author | Oumoulylte Mariame El Allaoui Ahmad Farhaoui Yousef Boughrous Ali Ait |
| author_facet | Oumoulylte Mariame El Allaoui Ahmad Farhaoui Yousef Boughrous Ali Ait |
| author_sort | Oumoulylte Mariame |
| collection | DOAJ |
| description | Air pollution is a serious concern for public health, linked to many diseases and an increase in fatalities. To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. This study explores how supervised machine learning methods can help predict air quality based on historicaland current environmental data. We assess the effectiveness of algorithms such as Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting. Important factors like PM2.5, PM10, NO2, SO2, and CO pollution levels are examined, along with weather elements like temperature and humidity. Our findings suggest that machine learning models can reliably forecast air quality, helping manage pollution and protect public health, with Random Forest showing the best results among the models tested. |
| format | Article |
| id | doaj-art-409c5daecd9c4f94b3bd3417646d34a6 |
| institution | Kabale University |
| issn | 2267-1242 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj-art-409c5daecd9c4f94b3bd3417646d34a62025-08-20T03:45:04ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016320201210.1051/e3sconf/202563202012e3sconf_joe52025_02012Efficient Air Quality Prediction Models Based on Supervised Machine Learning TechniquesOumoulylte Mariame0El Allaoui Ahmad1Farhaoui Yousef2Boughrous Ali Ait3IMIA laboratory, T-IDMS, FST Errachidia, Moulay Ismail University of MeknesIMIA laboratory, T-IDMS, FST Errachidia, Moulay Ismail University of MeknesIMIA laboratory, T-IDMS, FST Errachidia, Moulay Ismail University of MeknesResearch Team: Bio-ressources, Environment and Health, Department of Biology, FST Errachidia, Moulay Ismail University of MeknesAir pollution is a serious concern for public health, linked to many diseases and an increase in fatalities. To tackle these issues, it's crucial to set up prediction systems allowing officials to act before high pollution levels occur. This study explores how supervised machine learning methods can help predict air quality based on historicaland current environmental data. We assess the effectiveness of algorithms such as Random Forest, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, and Gradient Boosting. Important factors like PM2.5, PM10, NO2, SO2, and CO pollution levels are examined, along with weather elements like temperature and humidity. Our findings suggest that machine learning models can reliably forecast air quality, helping manage pollution and protect public health, with Random Forest showing the best results among the models tested.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/32/e3sconf_joe52025_02012.pdf |
| spellingShingle | Oumoulylte Mariame El Allaoui Ahmad Farhaoui Yousef Boughrous Ali Ait Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques E3S Web of Conferences |
| title | Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques |
| title_full | Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques |
| title_fullStr | Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques |
| title_full_unstemmed | Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques |
| title_short | Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques |
| title_sort | efficient air quality prediction models based on supervised machine learning techniques |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/32/e3sconf_joe52025_02012.pdf |
| work_keys_str_mv | AT oumoulyltemariame efficientairqualitypredictionmodelsbasedonsupervisedmachinelearningtechniques AT elallaouiahmad efficientairqualitypredictionmodelsbasedonsupervisedmachinelearningtechniques AT farhaouiyousef efficientairqualitypredictionmodelsbasedonsupervisedmachinelearningtechniques AT boughrousaliait efficientairqualitypredictionmodelsbasedonsupervisedmachinelearningtechniques |