Ozone Level Prediction with Machine Learning Algorithms
The ozone level in the atmosphere affects the quality of life of all living things as well as it can be a hazard to human health and the environment. Ozone is a gas that absorbs most of the ultraviolet radiation reaching the Earth from the Sun. However, when the ozone level exceeds a certain thresho...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Turkish Air Force Academy
2021-07-01
|
| Series: | Havacılık ve Uzay Teknolojileri Dergisi |
| Subjects: | |
| Online Access: | http://jast.hho.edu.tr/index.php/JAST/article/view/469 |
| _version_ | 1797915409425367040 |
|---|---|
| author | Atınç YILMAZ |
| author_facet | Atınç YILMAZ |
| author_sort | Atınç YILMAZ |
| collection | DOAJ |
| description | The ozone level in the atmosphere affects the quality of life of all living things as well as it can be a hazard to human health and the environment. Ozone is a gas that absorbs most of the ultraviolet radiation reaching the Earth from the Sun. However, when the ozone level exceeds a certain threshold, risks would be exacerbated. Using machine learning algorithms can help to reduce risks, making inferences from earlier obtained data even for situations, which have not encountered before. In this study, a two-phased hybrid machine learning algorithm is proposed. It helps to predict the ozone level prospectively and reduce the risks. In the first stage, clustering is made with the method of genetic algorithms and the clustering result is transmitted as an introduction to the XGBoost classifier method. To check that the proposed model is applicable, support vector machine, random forest, multi-layered neural networks and XGBoost methods, which are among the frequently used machine learning methods, have been applied and the results were compared. After the 10-fold validation applied, the proposed model reached the most successful accuracy rate with 94%. |
| first_indexed | 2024-04-10T12:41:14Z |
| format | Article |
| id | doaj.art-f3ead6b86c4642e9911ee804159417a8 |
| institution | Directory Open Access Journal |
| issn | 1304-0448 1304-0448 |
| language | English |
| last_indexed | 2024-04-10T12:41:14Z |
| publishDate | 2021-07-01 |
| publisher | Turkish Air Force Academy |
| record_format | Article |
| series | Havacılık ve Uzay Teknolojileri Dergisi |
| spelling | doaj.art-f3ead6b86c4642e9911ee804159417a82023-02-15T16:14:19ZengTurkish Air Force AcademyHavacılık ve Uzay Teknolojileri Dergisi1304-04481304-04482021-07-01142177183Ozone Level Prediction with Machine Learning AlgorithmsAtınç YILMAZ0https://orcid.org/0000-0003-0038-7519Beykent UniversityThe ozone level in the atmosphere affects the quality of life of all living things as well as it can be a hazard to human health and the environment. Ozone is a gas that absorbs most of the ultraviolet radiation reaching the Earth from the Sun. However, when the ozone level exceeds a certain threshold, risks would be exacerbated. Using machine learning algorithms can help to reduce risks, making inferences from earlier obtained data even for situations, which have not encountered before. In this study, a two-phased hybrid machine learning algorithm is proposed. It helps to predict the ozone level prospectively and reduce the risks. In the first stage, clustering is made with the method of genetic algorithms and the clustering result is transmitted as an introduction to the XGBoost classifier method. To check that the proposed model is applicable, support vector machine, random forest, multi-layered neural networks and XGBoost methods, which are among the frequently used machine learning methods, have been applied and the results were compared. After the 10-fold validation applied, the proposed model reached the most successful accuracy rate with 94%.http://jast.hho.edu.tr/index.php/JAST/article/view/469machine learning algorithmsozone levelrisk predictionproposed modelk-fold cross validation |
| spellingShingle | Atınç YILMAZ Ozone Level Prediction with Machine Learning Algorithms Havacılık ve Uzay Teknolojileri Dergisi machine learning algorithms ozone level risk prediction proposed model k-fold cross validation |
| title | Ozone Level Prediction with Machine Learning Algorithms |
| title_full | Ozone Level Prediction with Machine Learning Algorithms |
| title_fullStr | Ozone Level Prediction with Machine Learning Algorithms |
| title_full_unstemmed | Ozone Level Prediction with Machine Learning Algorithms |
| title_short | Ozone Level Prediction with Machine Learning Algorithms |
| title_sort | ozone level prediction with machine learning algorithms |
| topic | machine learning algorithms ozone level risk prediction proposed model k-fold cross validation |
| url | http://jast.hho.edu.tr/index.php/JAST/article/view/469 |
| work_keys_str_mv | AT atıncyilmaz ozonelevelpredictionwithmachinelearningalgorithms |