Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region
Time series forecasting is relevant in many fields of human activity. In particular, when studying the processes associated with global warming, such forecasts are very important. The present study used data of the concentration of the greenhouse gases (methane) in the surface layer of atmospheric a...
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American Institute of Physics Inc.
2018
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fturalfuniv:oai:elar.urfu.ru:10995/75034 2024-01-21T10:02:33+01:00 Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region Sergeev, A. Shichkin, A. Buevich, A. 2018 application/pdf http://elar.urfu.ru/handle/10995/75034 https://aip.scitation.org/doi/pdf/10.1063/1.5082120 http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85058791958 https://doi.org/10.1063/1.5082120 en eng American Institute of Physics Inc. Sergeev A. Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region / A. Sergeev, A. Shichkin, A. Buevich // AIP Conference Proceedings. — 2018. — Vol. 2048. — 60005. 0094-243X https://aip.scitation.org/doi/pdf/10.1063/1.5082120 1 6f4a8931-5bda-4030-8890-a40c9512c2cf http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85058791958 http://elar.urfu.ru/handle/10995/75034 38629137 doi:10.1063/1.5082120 85058791958 000468108800102 info:eu-repo/semantics/openAccess AIP Conference Proceedings Conference Paper info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion 2018 fturalfuniv https://doi.org/10.1063/1.5082120 2023-12-26T01:50:52Z Time series forecasting is relevant in many fields of human activity. In particular, when studying the processes associated with global warming, such forecasts are very important. The present study used data of the concentration of the greenhouse gases (methane) in the surface layer of atmospheric air on the Arctic island Belyi, Russia. For the work, the time interval of 170 hours (about a week) was chosen during the summer period, characterized by significant daily fluctuations of methane concentration. Models based on artificial neural networks (ANN) such as Nonlinear Autoregressive Neural Network with an External Input (NARX), Elman Neural Network (ENN), and Multi-Layer Perceptron (MLP) were used for modelling. Methane concentrations corresponding to the first 150 hours of the interval used for ANN training, then the concentrations were predicted for the next 20 hours. The model based on the ANN type NARX showed the best accuracy. © 2018 Author(s). Conference Object Arctic Arctic Global warming Ural Federal University (URFU): ELAR Arctic Arctic Island ENVELOPE(-74.766,-74.766,62.234,62.234) AIP Conference Proceedings, 2048 060005 |
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Ural Federal University (URFU): ELAR |
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fturalfuniv |
language |
English |
description |
Time series forecasting is relevant in many fields of human activity. In particular, when studying the processes associated with global warming, such forecasts are very important. The present study used data of the concentration of the greenhouse gases (methane) in the surface layer of atmospheric air on the Arctic island Belyi, Russia. For the work, the time interval of 170 hours (about a week) was chosen during the summer period, characterized by significant daily fluctuations of methane concentration. Models based on artificial neural networks (ANN) such as Nonlinear Autoregressive Neural Network with an External Input (NARX), Elman Neural Network (ENN), and Multi-Layer Perceptron (MLP) were used for modelling. Methane concentrations corresponding to the first 150 hours of the interval used for ANN training, then the concentrations were predicted for the next 20 hours. The model based on the ANN type NARX showed the best accuracy. © 2018 Author(s). |
format |
Conference Object |
author |
Sergeev, A. Shichkin, A. Buevich, A. |
spellingShingle |
Sergeev, A. Shichkin, A. Buevich, A. Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
author_facet |
Sergeev, A. Shichkin, A. Buevich, A. |
author_sort |
Sergeev, A. |
title |
Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
title_short |
Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
title_full |
Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
title_fullStr |
Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
title_full_unstemmed |
Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region |
title_sort |
time series forecasting of methane concentrations in the surface layer of atmospheric air in arctic region |
publisher |
American Institute of Physics Inc. |
publishDate |
2018 |
url |
http://elar.urfu.ru/handle/10995/75034 https://aip.scitation.org/doi/pdf/10.1063/1.5082120 http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85058791958 https://doi.org/10.1063/1.5082120 |
long_lat |
ENVELOPE(-74.766,-74.766,62.234,62.234) |
geographic |
Arctic Arctic Island |
geographic_facet |
Arctic Arctic Island |
genre |
Arctic Arctic Global warming |
genre_facet |
Arctic Arctic Global warming |
op_source |
AIP Conference Proceedings |
op_relation |
Sergeev A. Time series forecasting of methane concentrations in the surface layer of atmospheric air in Arctic region / A. Sergeev, A. Shichkin, A. Buevich // AIP Conference Proceedings. — 2018. — Vol. 2048. — 60005. 0094-243X https://aip.scitation.org/doi/pdf/10.1063/1.5082120 1 6f4a8931-5bda-4030-8890-a40c9512c2cf http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85058791958 http://elar.urfu.ru/handle/10995/75034 38629137 doi:10.1063/1.5082120 85058791958 000468108800102 |
op_rights |
info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1063/1.5082120 |
container_title |
AIP Conference Proceedings, |
container_volume |
2048 |
container_start_page |
060005 |
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1788692855390208000 |