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<sub>2</sub>, CH<sub>4</sub>) intensity in a certain area of the cryolithozone using data from a geographically distributed network of mul...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Andrey V. Timofeev, Viktor Y. Piirainen, Vladimir Y. Bazhin, Aleksander B. Titov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	CO<sub>2</sub> CH<sub>4</sub> hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9458b94754cb47a8992ff907bcf169ef
record_format	dspace
spelling	oai:doaj.org-article:9458b94754cb47a8992ff907bcf169ef2021-11-25T16:45:07ZOperational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone10.3390/atmos121114662073-4433https://doaj.org/article/9458b94754cb47a8992ff907bcf169ef2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-4433/12/11/1466https://doaj.org/toc/2073-4433We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO<sub>2</sub>, CH<sub>4</sub>) 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<sub>2</sub>, CH<sub>4</sub> 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.Andrey V. TimofeevViktor Y. PiirainenVladimir Y. BazhinAleksander B. TitovMDPI AGarticleCO<sub>2</sub>CH<sub>4</sub>hydrocarbon emission predictionmultimodal sensormachine learningXGBoostMeteorology. ClimatologyQC851-999ENAtmosphere, Vol 12, Iss 1466, p 1466 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CO<sub>2</sub> CH<sub>4</sub> hydrocarbon emission prediction multimodal sensor machine learning XGBoost Meteorology. Climatology QC851-999
spellingShingle	CO<sub>2</sub> CH<sub>4</sub> 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
description	We proposed a new approach to solving the problem of operational analysis and medium-term forecasting of the greenhouse gas generation (CO<sub>2</sub>, CH<sub>4</sub>) 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<sub>2</sub>, CH<sub>4</sub> 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
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://doaj.org/article/9458b94754cb47a8992ff907bcf169ef
work_keys_str_mv	AT andreyvtimofeev operationalanalysisandmediumtermforecastingofthegreenhousegasgenerationintensityinthecryolithozone AT viktorypiirainen operationalanalysisandmediumtermforecastingofthegreenhousegasgenerationintensityinthecryolithozone AT vladimirybazhin operationalanalysisandmediumtermforecastingofthegreenhousegasgenerationintensityinthecryolithozone AT aleksanderbtitov operationalanalysisandmediumtermforecastingofthegreenhousegasgenerationintensityinthecryolithozone
_version_	1718413032680325120

Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone

Ejemplares similares