Yafouz, Ayman and AlDahoul, Nouar and Birima, Ahmed H. and Ahmed, Ali Najah and Sherif, Mohsen and Sefelnasr, Ahmed and Allawi, Mohammed Falah and Ahmed ElShafie, Ahmed Hussein Kamel (2022) Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction. Alexandria Engineering Journal, 61 (6). pp. 4607-4622. ISSN 1110-0168, DOI https://doi.org/10.1016/j.aej.2021.10.021.
Full text not available from this repository.Abstract
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 (O-3) 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 (O-3) 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, R-2, and training time. The results indicated that LR, SVR, GPR and ANN were able to give the highest R-2 (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 R-2 (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 R-2, RMSE and MAE. Ensemble regression models were found to balance between high prediction accuracy in terms of R-2 and low training time and thus considered as a feasible solution for application of Ozone concentration prediction using the data in hourly scenario. (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
| Item Type: | Article |
|---|---|
| Funders: | Department Of Environment (DOE), Malaysia |
| Uncontrolled Keywords: | Air quality; Ozone concentration prediction; Machine learning; Hyperparameter optimization |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 28 Jul 2022 01:24 |
| Last Modified: | 28 Jul 2022 01:24 |
| URI: | http://eprints.um.edu.my/id/eprint/33623 |
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