One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level
With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentr...
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| Format: | Article |
| Language: | English |
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Kyrgyz Turkish Manas University
2021-06-01
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| Series: | MANAS: Journal of Engineering |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/1538935 |
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| author | Ercan Avşar Waleed Mahmood |
| author_facet | Ercan Avşar Waleed Mahmood |
| author_sort | Ercan Avşar |
| collection | DOAJ |
| description | With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. The models make the prediction using concentrations of six atmospheric components (PM2.5, PM10, Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO)). The utilized machine learning methods are multilayer perception (MLP), Support Vector Regression (SVM), k-Nearest Neighbor (K-NN), Random Forests (RF), Gradient Boosting (GB), and Elastic Net (EN). After the predictions made by these models, the predicted values were further processed to be classified into one of the six air quality levels defined by United States Environmental Protection Agency. The prediction performances of the models as well as their corresponding classification results were analyzed. It was shown that MLP model gives the lowest RMSE of 2246 for prediction step while SVR achieved the highest accuracy score of 0.790. |
| format | Article |
| id | doaj-art-08d53e32fab94bbda01abeb0c4153a8f |
| institution | Kabale University |
| issn | 1694-7398 |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Kyrgyz Turkish Manas University |
| record_format | Article |
| series | MANAS: Journal of Engineering |
| spelling | doaj-art-08d53e32fab94bbda01abeb0c4153a8f2025-02-03T12:07:27ZengKyrgyz Turkish Manas UniversityMANAS: Journal of Engineering1694-73982021-06-0191455410.51354/mjen.8697361437One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality LevelErcan Avşar0https://orcid.org/0000-0002-1356-2753Waleed Mahmood1https://orcid.org/0000-0002-4973-0106Çukurova University , Fen Bilimleri Enstitüsü, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ (İNGİLİZCE)Çukurova University , Fen Bilimleri Enstitüsü, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ (İNGİLİZCE)With the rapid spread of urbanization, competent authorities become increasingly anxious from air pollution risks and effect on citizens especially those with respiratory diseases. In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. The models make the prediction using concentrations of six atmospheric components (PM2.5, PM10, Ozone (O3), Sulfur Dioxide (SO2), Nitrogen Dioxide (NO2), and Carbon Monoxide (CO)). The utilized machine learning methods are multilayer perception (MLP), Support Vector Regression (SVM), k-Nearest Neighbor (K-NN), Random Forests (RF), Gradient Boosting (GB), and Elastic Net (EN). After the predictions made by these models, the predicted values were further processed to be classified into one of the six air quality levels defined by United States Environmental Protection Agency. The prediction performances of the models as well as their corresponding classification results were analyzed. It was shown that MLP model gives the lowest RMSE of 2246 for prediction step while SVR achieved the highest accuracy score of 0.790.https://dergipark.org.tr/en/download/article-file/1538935machine learningtime-series forecastingregression methodssequence-to-sequenceair quality index. |
| spellingShingle | Ercan Avşar Waleed Mahmood One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level MANAS: Journal of Engineering machine learning time-series forecasting regression methods sequence-to-sequence air quality index. |
| title | One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level |
| title_full | One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level |
| title_fullStr | One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level |
| title_full_unstemmed | One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level |
| title_short | One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level |
| title_sort | one step ahead prediction of ozone concentration for determination of outdoor air quality level |
| topic | machine learning time-series forecasting regression methods sequence-to-sequence air quality index. |
| url | https://dergipark.org.tr/en/download/article-file/1538935 |
| work_keys_str_mv | AT ercanavsar onestepaheadpredictionofozoneconcentrationfordeterminationofoutdoorairqualitylevel AT waleedmahmood onestepaheadpredictionofozoneconcentrationfordeterminationofoutdoorairqualitylevel |