(Article of periodic en Anglais - 2014)

Document title

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

Authors(s) and Affiliation(s)

(1) Dept. of Environmental Engineering, Abant Izzet Baysal Univ., Bolu, TURQUIE


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


Article of periodic

published at : Geographical analysis / ISSN 0016-7363

Editor : Ohio State University Press, Columbus, OH - ETATS-UNIS (1969)

Millesime : 2014, vol. 46, no1 [pp. 37-52]

Bibliographic references : 2 p.

Collation : 5 fig., 4 tabl.



INIST-CNRS, Cote INIST : 17078

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1111/gean.12025

Tous droits réservés © Prodig - Bibliographie Géographique Internationale (BGI), 2014
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