A novel framework for spatio-temporal prediction of environmental data using deep learning
Abstract As the role played by statistical and computational sciences in climate and environmental modelling and prediction becomes more important, Machine Learning researchers are becoming more aware of the relevance of their work to help tackle the climate crisis. Indeed, being universal nonlinear...
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Auteurs principaux: | Federico Amato, Fabian Guignard, Sylvain Robert, Mikhail Kanevski |
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Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2020
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Accès en ligne: | https://doaj.org/article/0057d3821f644db5b0eb8fbd457f41e3 |
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