Rainfall to Streamflow Modelling
Adapting to climate change implies the intelligent management of water resources and waterway infrastructure. We operate a model that predict the amount of water in rivers, by integrating the weather forecast with location information from a variety of maps. This information can be leveraged directly generate local flood warnings or plan water way logistics. The model is flexible and can also be adapted to other application cases such as prediction soil moisture for agriculture or creating forest fire hazard warnings.
Nowcasting
Nowcasting is the very short‑term forecasting of atmospheric conditions over the next minutes to a few hours. It is critical for issuing timely severe‑weather warnings including tornadoes, flash floods and hailstorms. Precipitation and extreme‑weather nowcasting are particularly challenging within the weather domain due to the highly non‑linear and chaotic nature of underlying processes, yielding low predictability.
As many phenomena are not well captured by conventional methods, deep learning poses a promising direction to deal with the vast amounts of data available in the weather domain. We develop multi-modal generative models to enable accurate uncertainty quantification, integrating data sources such as radar reflectivity, high‑resolution satellite imagery and physics‑based variables such as wind velocity, humidity and temperature.
Human-Centered AI for Crisis and Disaster Management
To ensure that our AI-based solutions are practical and usable, we take user needs into account. Together with partners from industry, healthcare, and the public sector, we develop user-friendly, interactive AI applications that meet real-world requirements. Our approach to human-centered AI development – reliable, trustworthy, and transparent – ensures that our systems comply with the European AI Act.
Publications
| [1] | Christopher J. Anders, David Neumann, Talmaj Marinč, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin. “XAI for Analyzing and Unlearning Spurious Correlations in ImageNet”. In: 2020 International Conference on Machine Learning. ICML. XXAI: Extending Explainable AI Beyond Deep Models and Classifiers (July 17, 2020). Fraunhofer Institute for Telecommunications, Heinrich-Hertz-Institut, HHI et al. July 7, 2020. URL: http://interpretable-ml.org/icml2020workshop/pdf/11.pdf (visited on 09/18/2024). Workshop papers not published in official proceedings. |
| [2] | Christopher J. Anders, Leander Weber, David Neumann, Wojciech Samek, Klaus-Robert Müller, and Sebastian Lapuschkin. “Finding and removing Clever Hans. Using explanation methods to debug and improve deep models”. In: Information Fusion 77 (Aug. 3, 2021), pp. 261–295. ISSN: 1566-2535. DOI: 10.1016/j.inffus.2021.07.015. |
| [3] | Maximilian Dreyer, Frederik Pahde, Christopher J. Anders, Wojciech Samek, and Sebastian Lapuschkin. “From Hope to Safety. Unlearning Biases of Deep Models via Gradient Penalization in Latent Space”. In: Proceedings of the AAAI Conference on Artificial Intelligence. The 38th Annual AAAI Conference on Artificial Intelligence. AAAI (Vancouver Convention Centre – West Building, Canada Pl, Vancouver, BC V6C 3G3, Feb. 20–27, 2024). Vol. 38. 19. Association for the Advancement of Artificial Intelligence. 1101 Pennsylvania Ave, NW, Suite 300, Washington, DC 20004, United States of America: AAAI Press, Mar. 24, 2024, pp. 21046–21046. ISBN: 978-1-57735-887-9. DOI: 10.1609/aaai.v38i19.30096. |
| [4] | Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde, Wojciech Samek, and Sebastian Lapuschkin. “Reactive Model Correction. Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression”. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Proceedings. 2024 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. CVPRW (Seattle, Washington, United States of America, June 17–18, 2024). IEEE Computer Society. 3 Park Avenue, 17th Floor, New York City, New York, United States of America: Institute of Electrical and Electronics Engineers (IEEE), Sept. 27, 2024, pp. 3532–3541. ISBN: 979-8-3503-6547-4. DOI: 10.1109/CVPRW63382.2024.00357. |