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Deep learning and process understanding for data-driven Earth system science.
Reichstein, Markus; Camps-Valls, Gustau; Stevens, Bjorn; Jung, Martin; Denzler, Joachim; Carvalhais, Nuno.
Afiliação
  • Reichstein M; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany. mreichstein@bgc-jena.mpg.de.
  • Camps-Valls G; Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany. mreichstein@bgc-jena.mpg.de.
  • Stevens B; Image Processing Laboratory (IPL), University of València, Valencia, Spain.
  • Jung M; Max Planck Institute for Meteorology, Hamburg, Germany.
  • Denzler J; Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany.
  • Carvalhais N; Michael-Stifel-Center Jena for Data-driven and Simulation Science, Jena, Germany.
  • Prabhat; Computer Vision Group, Computer Science, Friedrich Schiller University, Jena, Germany.
Nature ; 566(7743): 195-204, 2019 02.
Article em En | MEDLINE | ID: mdl-30760912
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Simulação por Computador / Reconhecimento Automatizado de Padrão / Ciências da Terra / Previsões / Aprendizado Profundo / Big Data Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: Nature Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Simulação por Computador / Reconhecimento Automatizado de Padrão / Ciências da Terra / Previsões / Aprendizado Profundo / Big Data Tipo de estudo: Prognostic_studies Limite: Female / Humans Idioma: En Revista: Nature Ano de publicação: 2019 Tipo de documento: Article País de afiliação: Alemanha