Please use this identifier to cite or link to this item: http://hdl.handle.net/10995/103064
Title: Two-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the narx neural network and subsequent prediction of residuals
Двухшаговый комбинированный алгоритм повышения точности прогнозирования концентрации метана в атмосферном воздухе на основе нейронной сети NARX и последующего прогнозирования невязок
Authors: Subbotina, I. E.
Buevich, A. G.
Sergeev, A. P.
Shichkin, A. V.
Baglaeva, E. M.
Remezova, M. S.
Issue Date: 2020
Publisher: Nuclear Safety Institute of the Russian Academy of Sciences
Citation: Two-step combined algorithm for improving the accuracy of predicting methane concentration in atmospheric air based on the narx neural network and subsequent prediction of residuals / I. E. Subbotina, A. G. Buevich, A. P. Sergeev, et al. — DOI 10.25283/2223-4594-2020-2-59-67 // Arktika: Ekologia i Ekonomika. — 2020. — Vol. 38. — Iss. 2. — P. 59-67.
Abstract: Climate change in the Arctic is great and can have a significant inverse effect on the global climate, which determines the global significance of climate change in the Arctic. To date, many issues regarding the mechanisms responsible for the rapid melting of Arctic ice and permafrost degradation have not been resolved. It is not known when and what consequences these changes will lead to. Assessing the relationship between global warming and greenhouse gas emissions is an important environmental challenge. Among the main greenhouse gases, the evolution and climate-forming role of the carbon dioxide have been studied. The data on the methane subcycle of the carbon cycle is much less. In the paper, the authors propose a two-step combined algorithm (NARXR) to improve the accuracy of predicting methane concentration in atmospheric air based on the NARX neural network and subsequent prediction of the residuals. Two commonly used models based on artificial neural networks (ANN) for predicting time series are compared to determine the most appropriate base model. Nonlinear autoregressive neural network with external input (NARX) and Elman’s neural network are used. For the forecast, the authors use data on the methane concentration (CH4) in the atmospheric surface layer on the Arctic Island of Bely (Russia). Data is selected for a time interval of 192 hours, because it is characterized by significant daily fluctuations in the concentration of CH4. Values corresponding to the first 168 hours of the interval are used to train the ANN, and then concentrations are predicted for the next 24 hours. The proposed approach shows more accurate forecast results. © Subbotina I. E., Buevich A. G., Sergeev A. P., Shichkin A. V., Baglaeva E. M., Remezova M. S., 2020.
Keywords: ARTIFICIAL NEURAL NETWORKS
GREENHOUSE GASES
NARX
RESIDUALS
URI: http://hdl.handle.net/10995/103064
Access: info:eu-repo/semantics/openAccess
SCOPUS ID: 85101779592
PURE ID: 13189404
66f1e11b-8f88-4e24-a09e-3f964d97b219
ISSN: 22234594
DOI: 10.25283/2223-4594-2020-2-59-67
metadata.dc.description.sponsorship: The authors are grateful to the Department of Science and Innovation of the Yamal-Nenets Autonomous District and to the NP Russian Center for the Development of the Arctic, city of Salekhard, for technical and logistical support of scientific expeditions to the Island of Bely. The authors also thank the reviewers for constructive criticism and useful recommendations that have improved the quality of article materials.
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