Modeling net ecosystem carbon dioxide exchange using temporal neural networks after wavelet denoising

Auteur(s) et Affiliation(s)



Description :
The potential of 6 temporal artificial neural networks (ANNs) augmented with and without 3 orthogonal wavelet functions was tested for predicting net ecosystem exchange of carbon dioxide (CO2) based on a long-term eddy covariance (EC) data set for a temperate peatland. Multiple comparisons were made of (1) temporal ANNs with and without discrete wavelet transform (DWT) denoising; (2) denoising with the orthogonal wavelet families of Daubechies, Coiflet, and Symmlet; (3) different decomposition levels; (4) time-delay neural network, time-lag recurrent network, and recurrent neural network; (5) online learning versus batch learning algorithms; and (6) diel, diurnal, and nocturnal periods. The coefficient of determination, root mean square error, and mean absolute error performance metrics were used for multiple comparisons based on training, cross-validation, and independent validation of the temporal ANNs as a function of 24 explanatory variables contained in an EC data set. Integration of the temporal ANNs and DWT denoising provided more accurate and precise estimates of net ecosystem CO2 exchange.


Type de document :
Article de périodique

Source :
Geographical analysis, issn : 0016-7363, 2014, vol. 46, n°. 1, p. 37-52, nombre de pages : 16, Références bibliographiques : 2 p.

Date :
2014

Editeur :
Pays édition : Etats-Unis, Columbus, OH, Ohio State University Press