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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Main Authors: Ercan Avşar, Waleed Mahmood
Format: Article
Language:English
Published: Kyrgyz Turkish Manas University 2021-06-01
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.
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institution Kabale University
issn 1694-7398
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publisher Kyrgyz Turkish Manas University
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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