Dr. Basil KraftProject Leader
Global diagnostic models
⇨ Project group website
Deep learning for time series modeling
Ecosystem and Earth System dynamics are often hard to model and predict and today, still many machine-learning approaches fail in encoding temporal dependencies that typically characterize dynamical natural systems. Yet, deep learning approaches in the time domain (e.g., Recurrent Neural Networks or Transformer models) are theoretically prepared to address such issues. I explore how these data-driven approaches can be used to better describe, predict, and understand Earth system processes on the land surface via the modeling of Earth observation data.
Hybrid modeling combines physically-based modeling and machine learning. The approach allows to use prior physical knowledge to regularize data-driven models and ultimately, to achieve better predicability of complex Earth system processes. At the same time, hybrid models are partially interpretable, allowing to gain scientific insights.
- 2022-present Project leader Hybrid and explainable deep learning (HDL) Group
- 2022-present Member of the USMILE group.
- 2017-present Member of the Global Diagnostic Modelling Research Group at the Max Planck Institute for Biogeochemistry in Jena.
- 2017-2022 External PhD student at the Chair of Remote Sensing Technology (Computer Vision Research Group), Technical University of Munich
- 2015-2017 MSc in Remote Sensing and Geographic information Science, University of Zurich (CH)
- 2010-2014 BSc in Geography, University of Zurich (CH)
- Kraft, B., Jung, M., Körner, M., Koirala, S., & Reichstein, M. (2022). Towards hybrid modeling of the global hydrological cycle. Hydrology and Earth System Sciences Discussions, 1-40.
- Kraft, B., Besnard, S., & Koirala, S. (2021). Emulating Ecological Memory with Recurrent Neural Networks. Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote Sensing, Climate Science, and Geosciences, 269-281.
- Kraft, B., Jung, M., Körner, M., and Reichstein, M. (2020). Hybrid modeling: fusion of a deep learning approach and a physics-based model for global hydrological modeling. ISPRS.
- Kraft, B., Jung, M., Körner, M., Requena Mesa, C., Cortés, J., and Reichstein, M., (2019). Identifying Dynamic Memory Effects on Vegetation State Using Recurrent Neural Networks. Frontiers in Big Data, 2, p.31.
- Reichstein, M., Besnard, S., Carvalhais, N., Gans, F., Jung, M., Kraft, B., and Mahecha, M. (2018). Modelling Landsurface Time-Series with Recurrent Neural Nets. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 7640-7643). IEEE.
- Requena-Mesa, C., Reichstein, M., Mahecha, M., Kraft, B., and Denzler, J. (2018). Predicting landscapes as seen from space from environmental conditions. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium (pp. 1768-1771). IEEE.
Conferences & workshops
- 2022 - EGU talk "Estimating global terrestrial water storage components by a physically constrained recurrent neural network"
- 2020 - EGU talk "Towards global hybrid hydrological modeling by fusing deep learning and a conceptual model"
- 2019 - Workshop on ecological memory effects (talk, participant), Oak Ridge National Labs (ORNL)
- 2019 - Introduction to Deep Learning (talk), Institute of Coastal Research, HZG
- 2019 - EGU talk "Identifying dynamic memory effects on vegetation state using recurrent neural networks"
- 2019 - Workshop "Deep Learning 101" (host) at the Earth System PhD Conference (ESPC) at the Max Planck Institute for Biogeochemistry
- 2019 - Co-organizer of the Earth System PhD Conference (ESPC) at the Max Planck Institute for Biogeochemistry
- 2018 - Co-chair of the "Deep Learning for Environmental Science & Ecology" session at the International Conference on Ecological Informatics (ICEI)