Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction
Ozone (O3) is one of the common air pollutants. An increase in the ozone concentration can adversely affect public health and the environment such as vegetation and crops. Therefore, atmospheric air quality monitoring systems were found to monitor and predict ozone concentration. Due to complex form...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016821006918 |
| _version_ | 1828223346916982784 |
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| author | Ayman Yafouz Nouar AlDahoul Ahmed H. Birima Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Mohammed Falah Allawi Ahmed Elshafie |
| author_facet | Ayman Yafouz Nouar AlDahoul Ahmed H. Birima Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Mohammed Falah Allawi Ahmed Elshafie |
| author_sort | Ayman Yafouz |
| collection | DOAJ |
| description | Ozone (O3) is one of the common air pollutants. An increase in the ozone concentration can adversely affect public health and the environment such as vegetation and crops. Therefore, atmospheric air quality monitoring systems were found to monitor and predict ozone concentration. Due to complex formation of ozone influenced by precursors of ozone (O3) and meteorological conditions, there is a need to examine and evaluate various machine learning (ML) models for ozone concentration prediction. This study aims to utilize various ML models including Linear Regression (LR), Tree Regression (TR), Support Vector Regression (SVR), Ensemble Regression (ER), Gaussian Process Regression (GPR) and Artificial Neural Networks Models (ANN) to predict tropospheric (O3) using ozone concentration dataset. The dataset was created by observing hourly average data from air quality monitoring systems in 3 different stations including Putrajaya, Kelang, and KL in 3 sites in Peninsular Malaysia. The prediction models have been trained on this dataset and validated by optimizing their hyperparameters. Additionally, the performance of models was evaluated in terms of RMSE, MAE, R2, and training time. The results indicated that LR, SVR, GPR and ANN were able to give the highest R2 (83 % and 89 %) with specific hyperparameters in stations Kelang and KL, respectively. On the other hand, SVR and ER outweigh other models in terms of R2 (79 %) in Putrajaya station. Overall, regardless slightly performance differences, several developed models were able to learn patterns well and provide good prediction performance in terms of R2, RMSE and MAE. Ensemble regression models were found to balance between high prediction accuracy in terms of R2 and low training time and thus considered as a feasible solution for application of Ozone concentration prediction using the data in hourly scenario. |
| first_indexed | 2024-04-12T17:06:13Z |
| format | Article |
| id | doaj.art-90b31e0804934e399dcf8c692dde9088 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-04-12T17:06:13Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-90b31e0804934e399dcf8c692dde90882022-12-22T03:23:55ZengElsevierAlexandria Engineering Journal1110-01682022-06-0161646074622Comprehensive comparison of various machine learning algorithms for short-term ozone concentration predictionAyman Yafouz0Nouar AlDahoul1Ahmed H. Birima2Ali Najah Ahmed3Mohsen Sherif4Ahmed Sefelnasr5Mohammed Falah Allawi6Ahmed Elshafie7Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, 43000 Selangor, MalaysiaFaculty of Engineering, Multimedia University, 63100 Cyberjaya, MalaysiaDepartment of Civil Engineering, College of Engineering, Qassim University, Unaizah, Saudi ArabiaInstitute of Energy Infrastructure (IEI),Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), 43000 Selangor, Malaysia; Corresponding author.Civil and Environmental Eng. Dept, College of Engineering, United Arab Emirates University, Al Ain P.O. Box. 15551, United Arab Emirates; National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box. 15551, United Arab EmiratesNational Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box. 15551, United Arab EmiratesNew Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar 64001, IraqDepartment of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603 Kuala Lumpur, MalaysiaOzone (O3) is one of the common air pollutants. An increase in the ozone concentration can adversely affect public health and the environment such as vegetation and crops. Therefore, atmospheric air quality monitoring systems were found to monitor and predict ozone concentration. Due to complex formation of ozone influenced by precursors of ozone (O3) and meteorological conditions, there is a need to examine and evaluate various machine learning (ML) models for ozone concentration prediction. This study aims to utilize various ML models including Linear Regression (LR), Tree Regression (TR), Support Vector Regression (SVR), Ensemble Regression (ER), Gaussian Process Regression (GPR) and Artificial Neural Networks Models (ANN) to predict tropospheric (O3) using ozone concentration dataset. The dataset was created by observing hourly average data from air quality monitoring systems in 3 different stations including Putrajaya, Kelang, and KL in 3 sites in Peninsular Malaysia. The prediction models have been trained on this dataset and validated by optimizing their hyperparameters. Additionally, the performance of models was evaluated in terms of RMSE, MAE, R2, and training time. The results indicated that LR, SVR, GPR and ANN were able to give the highest R2 (83 % and 89 %) with specific hyperparameters in stations Kelang and KL, respectively. On the other hand, SVR and ER outweigh other models in terms of R2 (79 %) in Putrajaya station. Overall, regardless slightly performance differences, several developed models were able to learn patterns well and provide good prediction performance in terms of R2, RMSE and MAE. Ensemble regression models were found to balance between high prediction accuracy in terms of R2 and low training time and thus considered as a feasible solution for application of Ozone concentration prediction using the data in hourly scenario.http://www.sciencedirect.com/science/article/pii/S1110016821006918Air qualityOzone concentration predictionMachine learningHyperparameter optimization |
| spellingShingle | Ayman Yafouz Nouar AlDahoul Ahmed H. Birima Ali Najah Ahmed Mohsen Sherif Ahmed Sefelnasr Mohammed Falah Allawi Ahmed Elshafie Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction Alexandria Engineering Journal Air quality Ozone concentration prediction Machine learning Hyperparameter optimization |
| title | Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| title_full | Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| title_fullStr | Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| title_full_unstemmed | Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| title_short | Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| title_sort | comprehensive comparison of various machine learning algorithms for short term ozone concentration prediction |
| topic | Air quality Ozone concentration prediction Machine learning Hyperparameter optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1110016821006918 |
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