Standard

Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere: book chapter. / Butorova, Anastasia; Buevich, Alexander; Shichkin, Andrey et al.
New Trends in the Applications of Differential Equations in Sciences: book. ed. / А. Slavova. Springer Cham, 2023. p. 383-388 (Springer Proceedings in Mathematics & Statistics; Vol. 412).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

Butorova, A, Buevich, A, Shichkin, A, Sergeev, A, Baglaeva, E, Sergeeva, M, Subbotina, I & Vasilev, J 2023, Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere: book chapter. in А Slavova (ed.), New Trends in the Applications of Differential Equations in Sciences: book. Springer Proceedings in Mathematics & Statistics, vol. 412, Springer Cham, pp. 383-388. https://doi.org/10.1007/978-3-031-21484-4_34

APA

Butorova, A., Buevich, A., Shichkin, A., Sergeev, A., Baglaeva, E., Sergeeva, M., Subbotina, I., & Vasilev, J. (2023). Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere: book chapter. In А. Slavova (Ed.), New Trends in the Applications of Differential Equations in Sciences: book (pp. 383-388). (Springer Proceedings in Mathematics & Statistics; Vol. 412). Springer Cham. https://doi.org/10.1007/978-3-031-21484-4_34

Vancouver

Butorova A, Buevich A, Shichkin A, Sergeev A, Baglaeva E, Sergeeva M et al. Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere: book chapter. In Slavova А, editor, New Trends in the Applications of Differential Equations in Sciences: book. Springer Cham. 2023. p. 383-388. (Springer Proceedings in Mathematics & Statistics). doi: 10.1007/978-3-031-21484-4_34

Author

Butorova, Anastasia ; Buevich, Alexander ; Shichkin, Andrey et al. / Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere : book chapter. New Trends in the Applications of Differential Equations in Sciences: book. editor / А. Slavova. Springer Cham, 2023. pp. 383-388 (Springer Proceedings in Mathematics & Statistics).

BibTeX

@inproceedings{a6b3a99fa00848f7bc545981d8b0b428,
title = "Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere: book chapter",
abstract = "The paper presents a forecast of changes in the methane content in the air in surface layer of the atmosphere. The forecast was made by the models based on the two most common types of artificial neural networks (ANN): A nonlinear autoregressive neural networks with exogenous inputs (NARX) and Elman neural network (ENN). For training, we used the Levenberg–Marquardt learning algorithm. The data were collected upon monitoring the greenhouse gases on Bely Island, Yamal-Nenets Autonomous Okrug, Russia. For the comparison, the three time intervals with the different patterns of changes in methane content were chosen. To assess the prediction accuracy of the models, we used the mean absolute error, mean square error, and the standardized measure of the model prediction error degree—the index of agreement. The model based on the artificial neural network NARX for all simulated intervals was the most accurate.",
author = "Anastasia Butorova and Alexander Buevich and Andrey Shichkin and Aleksandr Sergeev and Elena Baglaeva and Marina Sergeeva and Irina Subbotina and Julian Vasilev",
note = "The equipment of the Common Use Center of Arctic Environmental Research of the Institute of Industrial Ecology of the Ural Branch of RAS was used to measure the concentration of greenhouse gases on Bely Island.",
year = "2023",
month = mar,
day = "18",
doi = "10.1007/978-3-031-21484-4_34",
language = "English",
isbn = "978-3-031-21483-7",
series = "Springer Proceedings in Mathematics & Statistics",
publisher = "Springer Cham",
pages = "383--388",
editor = "А. Slavova",
booktitle = "New Trends in the Applications of Differential Equations in Sciences",
address = "United Kingdom",

}

RIS

TY - GEN

T1 - Prediction of the Time Series by the Various Types of Artificial Neural Networks by the Example of Different Time Intervals of the Content of Methane in the Atmosphere

T2 - book chapter

AU - Butorova, Anastasia

AU - Buevich, Alexander

AU - Shichkin, Andrey

AU - Sergeev, Aleksandr

AU - Baglaeva, Elena

AU - Sergeeva, Marina

AU - Subbotina, Irina

AU - Vasilev, Julian

N1 - The equipment of the Common Use Center of Arctic Environmental Research of the Institute of Industrial Ecology of the Ural Branch of RAS was used to measure the concentration of greenhouse gases on Bely Island.

PY - 2023/3/18

Y1 - 2023/3/18

N2 - The paper presents a forecast of changes in the methane content in the air in surface layer of the atmosphere. The forecast was made by the models based on the two most common types of artificial neural networks (ANN): A nonlinear autoregressive neural networks with exogenous inputs (NARX) and Elman neural network (ENN). For training, we used the Levenberg–Marquardt learning algorithm. The data were collected upon monitoring the greenhouse gases on Bely Island, Yamal-Nenets Autonomous Okrug, Russia. For the comparison, the three time intervals with the different patterns of changes in methane content were chosen. To assess the prediction accuracy of the models, we used the mean absolute error, mean square error, and the standardized measure of the model prediction error degree—the index of agreement. The model based on the artificial neural network NARX for all simulated intervals was the most accurate.

AB - The paper presents a forecast of changes in the methane content in the air in surface layer of the atmosphere. The forecast was made by the models based on the two most common types of artificial neural networks (ANN): A nonlinear autoregressive neural networks with exogenous inputs (NARX) and Elman neural network (ENN). For training, we used the Levenberg–Marquardt learning algorithm. The data were collected upon monitoring the greenhouse gases on Bely Island, Yamal-Nenets Autonomous Okrug, Russia. For the comparison, the three time intervals with the different patterns of changes in methane content were chosen. To assess the prediction accuracy of the models, we used the mean absolute error, mean square error, and the standardized measure of the model prediction error degree—the index of agreement. The model based on the artificial neural network NARX for all simulated intervals was the most accurate.

UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85151057834

U2 - 10.1007/978-3-031-21484-4_34

DO - 10.1007/978-3-031-21484-4_34

M3 - Conference contribution

SN - 978-3-031-21483-7

T3 - Springer Proceedings in Mathematics & Statistics

SP - 383

EP - 388

BT - New Trends in the Applications of Differential Equations in Sciences

A2 - Slavova, А.

PB - Springer Cham

ER -

ID: 37097743