학술논문
Training Algorithms for Artificial Neural Networks for Time Series Forecasting of Greenhouse Gas Concentrations.
Document Type
Article
Author
Source
Subject
*ARTIFICIAL neural networks
*TIME series analysis
*GREENHOUSE gases
*ATMOSPHERIC layers
*ALGORITHMS
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Language
ISSN
0094-243X
Abstract
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]