Arrangör/Organiser: Stockholm Resilience Centre with partners
Webbadress/Webpage: https://stockholmresilience.org/news--events/seminars-and-events/stockholm-seminars/previous-seminars/2019/ss-2019/2019-08-05-deep-learning-for-a-better-understanding-of-the-earth-system.html
Kontakt/Contact: Stockholm Resilience Centre
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The Earth is a complex dynamic networked system. Machine learning, i.e. derivation of computational models from data, has already made important contributions to predict and understand components of the Earth system, specifically in climate, remote sensing and environmental sciences.

For instance, classifications of land cover types, prediction of land-atmosphere and ocean-atmosphere exchange, or detection of extreme events have greatly benefited from these approaches. Such data-driven information has already changed how Earth system models are evaluated and further developed.

However, many studies have not yet sufficiently addressed and exploited dynamic aspects of systems, such as memory effects for prediction and effects of spatial context, e.g. for classification and change detection. In particular new developments in deep learning offer great potential to overcome these limitations.

Yet, a key challenge and opportunity is to integrate (physical-biological) system modeling approaches with machine learning into hybrid modeling approaches, which combines physical consistency and machine learning versatility. A couple of examples are given, where the combination of system-based and machine-learning-based modelling helps our understanding of aspects of the Earth system.

About Markus Reichstein
Markus Reichstein is director of the Biogeochemical Integration Department at the Max-Planck Institute for Biogeochemistry, Jena, Professor for Global Geoecology at the FSU Jena, and Director at the Michael-Stifel-Center Jena for Data-driven and Simulation Science in Jena. His main research interests include the effect of climate variability/extreme and change on global ecosystems, in particular carbon and water cycles.