11 citations · 18 across the 5 of their papers we have counts for
4 papers · 1 filter
Using LSTMs for climate change assessment studies on droughts and floods
Frederik Kratzert, Daniel Klotz, Johannes Brandstetter +3
Climate change affects occurrences of floods and droughts worldwide. However, predicting climate impacts over individual watersheds is difficult, primarily because accurate hydrolo…
Accurate Hydrologic Modeling Using Less Information
Guy Shalev, Ran El-Yaniv, Daniel Klotz +3
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. R…
Towards Learning Universal, Regional, and Local Hydrological Behaviors via Machine-Learning Applied to Large-Sample Datasets
Frederik Kratzert, Daniel Klotz, Guy Shalev +3
Regional rainfall-runoff modeling is an old but still mostly out-standing problem in Hydrological Sciences. The problem currently is that traditional hydrological models degrade si…
NeuralHydrology -- Interpreting LSTMs in Hydrology
Frederik Kratzert, Mathew Herrnegger, Daniel Klotz +2
Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the in…