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...
| Published in: | AIP Conference Proceedings, |
|---|---|
| Main Authors: | , , |
| Format: | Conference Object |
| Language: | English |
| Published: |
American Institute of Physics Inc.
2018
|
| Subjects: | |
| Online Access: | 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 |
| Summary: | 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). |
|---|