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In our work, we compared two approaches for predicting changes in the concentration of one of the main greenhouse gases - methane. The study is based on surface methane concentration data obtained by monitoring the dynamics of changes in major greenhouse gases on the Arctic Island Belyy, Russia. We used a nonlinear autoregressive neural network with an external input (NARX), and a vector regression model. An artificial neural network type NARX was more accurate for predicting methane concentration changes. [ABSTRACT FROM AUTHOR]

In this work, the annual cycle and interannual variability of the content of greenhouse gases were studied in the Arctic regions of Russia. Gases concentrations (methane, water vapor) and main meteorological parameters (temperature, atmospheric pressure) were measured on the Arctic Island Belyy, Russia in July-August, 2015-2017. The daily mean concentrations of greenhouse gases were not differed within the mean plus or minus standard deviation year to year. The autoregressive models and the spectral Fourier analysis were built for determining the pattern of the dynamic changes in the time series Spearman's rank correlation coefficients between the greenhouse gases concentrations and temperature. The analysis of the dependence of Spearman's rank correlation coefficients on the time delay of the temperature reaction to a change in the content of greenhouse gases in the air revealed four invariants: one, three, seven, and ten days. There were explanations for the daily, weekly and ten-day rhythms, but the three-day one requires further explanation. [ABSTRACT FROM AUTHOR]

The paper predicts the changes in the concentration of one of the main greenhouse gases - methane (CH4). The forecast was made for three different time periods, each of which had its own characteristics of the dynamics of changes in the concentration of CH4. Data for the study were collected while monitoring the content of the main greenhouse gases in the surface layer of atmospheric air in the Russian Arctic (Bely Island, Yamalo-Nenets Autonomous Okrug). We compared the results of the models prediction based on the two types of artificial neural networks: Elman and nonlinear autoregressive neural network with external input (NARX). NARX showed a high prediction accuracy for all studied time intervals. [ABSTRACT FROM AUTHOR]

The paper considered the use of one of the most accurate artificial neural networks for predicting time series - a nonlinear autoregressive neural network with external input (NARX) for predicting the dynamics of changes in the concentrations of the main greenhouse gases. The data were obtained in the course of monitoring the dynamics of changes in the main greenhouse gases on the Arctic island Belyy, Russia. The data of the surface concentration of methane, carbon dioxide, carbon monoxide and water vapor were used. A time interval of 168 hours was chosen for the study during the summer period (July-August 2016). The NARX model accurately predicted concentration changes for all greenhouse gases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When studying the processes associated with global warming, forecasts of time series are very important. The present study used data of the concentration of greenhouse gas methane in the surface layer of atmospheric air on the Arctic island Belyi, Russia. For the work, a time interval of 170 hours was chosen. For the modelling, a model based on a nonlinear autoregressive neural network with an external input (NARX) was used. As a training algorithm three types were applied: Levenberg-Marquart (LM), LM with Bayesian regularization (BR), and a gradient descent with a speed parameter setting (GDA). Methane concentrations corresponding to the first 150 hours of the interval used to train the NARX network, then the concentrations were predicted for the next 20 hours. The model using the training algorithm LM was the most accurate. [ABSTRACT FROM AUTHOR]

The work is devoted to the forecasting of the time series by artificial neural networks (ANNs). The data were obtained as a result of the monitoring of greenhouse gases on Belyi Island, Yamalo-Nenetsky AO, Russia. For prediction, a model based on a Nonlinear Autoregressive Neural Network with an External Input (NARX) and Multi-Layer Perceptron (MLP) was used. Based on the concentrations of greenhouse gas methane, a forecast for a certain time interval was done, and a time horizon was established for the forecasting. The model based on the ANN type NARX showed the best results. [ABSTRACT FROM AUTHOR]