(1) Development of novel deep learning models or modeling workflows.
(2) Probing, exploring and improving our understanding of the (internal) states/representations of deep learning models to improve models and/or gain system insights.
(3) Understanding the reliability of deep learning, e.g., under non-stationarity and climate change.
(4) Modeling human behavior and impacts on the hydrological cycle.
(5) Deep Learning approaches for extreme event analysis, detection, and mitigation.
(6) Natural Language Processing in support of models and/or modeling workflows.
(7) Applications of Large Language Models and Large Multimodal Models (e.g. ChatGPT, Gemini, etc.) in the context of hydrology.
(8) Uncertainty estimation for and with Deep Learning.
(9) Advances towards foundational models in the context of hydrology and Earth Sciences more generally.
(10) Exploration of different training strategies, such as self-supervised learning, unsupervised learning, and reinforcement learning.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
EGU25-15731 | ECS | Posters virtual | VPS9
Data-driven models for streamflow regionalization in Krishna River Basin, IndiaTue, 29 Apr, 14:00–15:45 (CEST) | vPA.4
EGU25-18458 | ECS | Posters virtual | VPS9
Can the catchment features influence the performance of the conceptual hydrological and deep learning models? A study using large sample hydrologic dataTue, 29 Apr, 14:00–15:45 (CEST) | vPA.5
EGU25-18599 | ECS | Posters virtual | VPS9
Downscaling MODIS ET using deep learningTue, 29 Apr, 14:00–15:45 (CEST) | vPA.6
EGU25-580 | ECS | Posters virtual | VPS9
Streamflow simulations using regionalized Long Short-Term Memory (LSTM) neural network models in contrasting climatic conditionsTue, 29 Apr, 14:00–15:45 (CEST) | vPA.26