Reconstructing the ozone concentration profile using machine learning methods
The main greenhouse gases are ozone and the gas components of ozone cycles. Operational determination of ozone concentration profiles is carried out by lidar methods, which limits the number of measurements obtained. Machine learning methods can be used to build predictive models of the data as well...
| Опубликовано в: : | Proceedings of SPIE Vol. 12341 : 28th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 2022, Tomsk, Russia. P. 123413L-1-123413L-5 |
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| Главный автор: | |
| Формат: | Статья в журнале |
| Язык: | English |
| Предметы: | |
| Online-ссылка: | http://vital.lib.tsu.ru/vital/access/manager/Repository/koha:001009330 Перейти в каталог НБ ТГУ |
| Итог: | The main greenhouse gases are ozone and the gas components of ozone cycles. Operational determination of ozone concentration profiles is carried out by lidar methods, which limits the number of measurements obtained. Machine learning methods can be used to build predictive models of the data as well as to approximate them. This paper investigates the possibility of generating data to build robust predictive models of ozone concentration profiles based on generative adversarial neural networks (GAN). Several GAN architectures were proposed and the benefits of each one is discussed. |
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| Библиография: | Библиогр.: 14 назв. |
| ISSN: | 0277-786X |