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 (CO2, CH4) intensity in a certain area of the cryolithozone using data from a geographically distributed network of multimodal measuring stations. A network of measu...
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ftmdpi:oai:mdpi.com:/2073-4433/12/11/1466/ 2023-08-20T04:09:13+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 agris 2021-11-05 application/pdf https://doi.org/10.3390/atmos12111466 EN eng Multidisciplinary Digital Publishing Institute Air Quality https://dx.doi.org/10.3390/atmos12111466 https://creativecommons.org/licenses/by/4.0/ Atmosphere; Volume 12; Issue 11; Pages: 1466 CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost Text 2021 ftmdpi https://doi.org/10.3390/atmos12111466 2023-08-01T03:10:16Z We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO2, CH4) 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 CO2, CH4 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. Text permafrost MDPI Open Access Publishing Atmosphere 12 11 1466 |
institution |
Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost |
spellingShingle |
CO 2 CH 4 hydrocarbon emission prediction multimodal sensor machine learning XGBoost 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 |
description |
We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO2, CH4) 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 CO2, CH4 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 |
Text |
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 |
Multidisciplinary Digital Publishing Institute |
publishDate |
2021 |
url |
https://doi.org/10.3390/atmos12111466 |
op_coverage |
agris |
genre |
permafrost |
genre_facet |
permafrost |
op_source |
Atmosphere; Volume 12; Issue 11; Pages: 1466 |
op_relation |
Air Quality https://dx.doi.org/10.3390/atmos12111466 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/atmos12111466 |
container_title |
Atmosphere |
container_volume |
12 |
container_issue |
11 |
container_start_page |
1466 |
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1774722031018835968 |