Abstract
The study tested how the wavelet transform of the data affects the accuracy of an artificial neural network model for forecasting surface methane concentration. A model based on the nonlinear autoregressive neural network with external input (NARX) was used. For comparison, we used the base NARX model and the hybrid model. The hybrid model was created based on the data to which the discrete wavelet transform (DWT) was applied. For DWT, the Daubechies wavelet of the fourth level was used. The initial data for the study were collected on the measurements of the concentration of greenhouse gases in the Russian Arctic zone. We evaluated the accuracy of the models by the following indicators: Mean absolute error, root mean square error, and the index of agreement. The proposed approach has improved the accuracy of the forecast. The accuracy of the hybrid model has increased by more than 10%.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.