Modeling moisture fluxes using artificial neural networks: can information extraction overcome data loss?
Eddy covariance sites can experience data losses as high as 30 to 45% on an annual basis. Artificial neural networks (ANNs) have been identified as powerful tools for gap filling, but their performance depends on the representativeness of data used to train the model. In this paper, we develop a nor...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2011-01-01
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| Series: | Hydrology and Earth System Sciences |
| Online Access: | http://www.hydrol-earth-syst-sci.net/15/359/2011/hess-15-359-2011.pdf |