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Deep learning and process understanding for data-driven Earth system science


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.

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Fig. 1: Big data challenges in the geoscientific context.
Fig. 2: Four examples of typical deep learning applications (left panels) and the geoscientific problems they can be applied to (right panels).
Fig. 3: Linkages between physical models and machine learning.
Fig. 4: Interpretation of hybrid modelling as deepening a deep learning architecture by adding one or several physical layers after the multilayer neural network to make the model more physically realistic.


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We thank D. Frank and L. Maack for proofreading, help with the literature and technical help, and F. Gans for programming help in Julia. This research was supported by a grant by the Alexander von Humboldt Foundation (Max Planck Research Prize) to M.R. G.C.-V. was supported by the European Research Council (ERC) under the ERC Consolidator Grant ERC-CoG-2014 SEDAL (grant agreement 647423).

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M.R. conceived the work, created a first outline with Prabhat and ran the numerical experiment. All authors wrote the manuscript.

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Correspondence to Markus Reichstein.

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Reichstein, M., Camps-Valls, G., Stevens, B. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).

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