@article{Reichstein2019:DLP,
    type = {article},
    key = {Reichstein2019:DLP},
    title = {Deep learning and process understanding for data-driven Earth system science},
    author = {Markus Reichstein and Gustau Camps-Valls and Bjorn Stevens and Martin Jung and Joachim Denzler and Nuno Carvalhais and Prabhat},
    journal = {Nature},
    year = {2019},
    month = {February},
    number = {7743},
    pages = {195-204},
    volume = {566},
    abstract = { 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.},
    doi = {10.1038/s41586-019-0912-1},
    issn = {1476-4687},
    url = {https://idw-online.de/de/news?id=710539},
}