Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone
We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO 2 , CH 4 ) intensity in a certain area of the cryolithozone using data from a geographically distributed network of multimodal measuring stations. A network of m...
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ftdoajarticles:oai:doaj.org/article:9458b94754cb47a8992ff907bcf169ef 2023-05-15T17:57:47+02:00 Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone Andrey V. Timofeev Viktor Y. Piirainen Vladimir Y. Bazhin Aleksander B. Titov 2021-11-01T00:00:00Z https://doi.org/10.3390/atmos12111466 https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef EN eng MDPI AG https://www.mdpi.com/2073-4433/12/11/1466 https://doaj.org/toc/2073-4433 doi:10.3390/atmos12111466 2073-4433 https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef Atmosphere, Vol 12, Iss 1466, p 1466 (2021) CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999 article 2021 ftdoajarticles https://doi.org/10.3390/atmos12111466 2022-12-31T09:08:19Z We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO 2 , CH 4 ) intensity in a certain area of the cryolithozone using data from a geographically distributed network of multimodal measuring stations. A network of measuring stations, capable of functioning autonomously for long periods of time, continuously generated a data flow of the CO 2 , CH 4 concentration, soil moisture, and temperature, as well as a number of other parameters. These data, taking into account the type of soil, were used to build a spatially distributed dynamic model of greenhouse gas emission intensity of the permafrost area depending on the temperature and moisture of the soil. This article presented models for estimating and medium-term predicting ground greenhouse gases emission intensity, which are based on artificial intelligence methods. The results of the numerical simulations were also presented, which showed the adequacy of the proposed approach for predicting the intensity of greenhouse gas emissions. Article in Journal/Newspaper permafrost Directory of Open Access Journals: DOAJ Articles Atmosphere 12 11 1466 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999 |
spellingShingle |
CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999 Andrey V. Timofeev Viktor Y. Piirainen Vladimir Y. Bazhin Aleksander B. Titov Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
topic_facet |
CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999 |
description |
We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO 2 , CH 4 ) intensity in a certain area of the cryolithozone using data from a geographically distributed network of multimodal measuring stations. A network of measuring stations, capable of functioning autonomously for long periods of time, continuously generated a data flow of the CO 2 , CH 4 concentration, soil moisture, and temperature, as well as a number of other parameters. These data, taking into account the type of soil, were used to build a spatially distributed dynamic model of greenhouse gas emission intensity of the permafrost area depending on the temperature and moisture of the soil. This article presented models for estimating and medium-term predicting ground greenhouse gases emission intensity, which are based on artificial intelligence methods. The results of the numerical simulations were also presented, which showed the adequacy of the proposed approach for predicting the intensity of greenhouse gas emissions. |
format |
Article in Journal/Newspaper |
author |
Andrey V. Timofeev Viktor Y. Piirainen Vladimir Y. Bazhin Aleksander B. Titov |
author_facet |
Andrey V. Timofeev Viktor Y. Piirainen Vladimir Y. Bazhin Aleksander B. Titov |
author_sort |
Andrey V. Timofeev |
title |
Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
title_short |
Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
title_full |
Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
title_fullStr |
Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
title_full_unstemmed |
Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone |
title_sort |
operational analysis and medium-term forecasting of the greenhouse gas generation intensity in the cryolithozone |
publisher |
MDPI AG |
publishDate |
2021 |
url |
https://doi.org/10.3390/atmos12111466 https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Atmosphere, Vol 12, Iss 1466, p 1466 (2021) |
op_relation |
https://www.mdpi.com/2073-4433/12/11/1466 https://doaj.org/toc/2073-4433 doi:10.3390/atmos12111466 2073-4433 https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef |
op_doi |
https://doi.org/10.3390/atmos12111466 |
container_title |
Atmosphere |
container_volume |
12 |
container_issue |
11 |
container_start_page |
1466 |
_version_ |
1766166288155017216 |