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Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks
Vegetation state is largely driven by climate and the complexity of involved processes leads to non-linear interactions over multiple time-scales. Recently, the role of temporally lagged dependencies, so-called memory effects, has been emphasized and studied using data-driven methods, relying on a v...
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| Publicat a: | Front Big Data |
|---|---|
| Autors principals: | , , , , , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Frontiers Media S.A.
2019
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7931900/ https://ncbi.nlm.nih.gov/pubmed/33693354 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fdata.2019.00031 |
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