Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction
Forecasting; Learning algorithms; Machine learning; Monitoring; Neural networks; Ozone; Public health; Regression analysis; Air quality monitoring; Artificial neural network modeling; Gaussian process regression; Hyper-parameter; Hyper-parameter optimizations; Machine learning models; Ozone concentr...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Elsevier B.V.
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-26874 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-268742023-05-29T17:37:26Z Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction Yafouz A. AlDahoul N. Birima A.H. Ahmed A.N. Sherif M. Sefelnasr A. Allawi M.F. Elshafie A. 57221981418 56656478800 23466519000 57214837520 7005414714 6505592467 57057678400 16068189400 Forecasting; Learning algorithms; Machine learning; Monitoring; Neural networks; Ozone; Public health; Regression analysis; Air quality monitoring; Artificial neural network modeling; Gaussian process regression; Hyper-parameter; Hyper-parameter optimizations; Machine learning models; Ozone concentration; Ozone concentrations predictions; Quality monitoring system; Support vector regressions; Air quality 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. � 2021 THE AUTHORS Final 2023-05-29T09:37:26Z 2023-05-29T09:37:26Z 2022 Article 10.1016/j.aej.2021.10.021 2-s2.0-85117767086 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117767086&doi=10.1016%2fj.aej.2021.10.021&partnerID=40&md5=f6438886e4ba4c42c136394976541809 https://irepository.uniten.edu.my/handle/123456789/26874 61 6 4607 4622 All Open Access, Gold Elsevier B.V. Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
Forecasting; Learning algorithms; Machine learning; Monitoring; Neural networks; Ozone; Public health; Regression analysis; Air quality monitoring; Artificial neural network modeling; Gaussian process regression; Hyper-parameter; Hyper-parameter optimizations; Machine learning models; Ozone concentration; Ozone concentrations predictions; Quality monitoring system; Support vector regressions; Air quality |
| author2 |
57221981418 |
| author_facet |
57221981418 Yafouz A. AlDahoul N. Birima A.H. Ahmed A.N. Sherif M. Sefelnasr A. Allawi M.F. Elshafie A. |
| format |
Article |
| author |
Yafouz A. AlDahoul N. Birima A.H. Ahmed A.N. Sherif M. Sefelnasr A. Allawi M.F. Elshafie A. |
| spellingShingle |
Yafouz A. AlDahoul N. Birima A.H. Ahmed A.N. Sherif M. Sefelnasr A. Allawi M.F. Elshafie A. Comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| author_sort |
Yafouz A. |
| title |
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_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_sort |
comprehensive comparison of various machine learning algorithms for short-term ozone concentration prediction |
| publisher |
Elsevier B.V. |
| publishDate |
2023 |
| _version_ |
1806426360346836992 |
| score |
13.214268 |