F
Frederik Kratzert
Researcher at Johannes Kepler University of Linz
Publications - 57
Citations - 1906
Frederik Kratzert is an academic researcher from Johannes Kepler University of Linz. The author has contributed to research in topics: Computer science & Environmental science. The author has an hindex of 11, co-authored 37 publications receiving 682 citations. Previous affiliations of Frederik Kratzert include Google & University of Natural Resources and Life Sciences, Vienna.
Papers
More filters
Journal ArticleDOI
Rainfall-runoff modelling using Long Short-Term Memory (LSTM) networks
TL;DR: In this paper, a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network, was proposed for modeling storage effects in e.g. catchments with snow influence.
Journal ArticleDOI
Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
Frederik Kratzert,Daniel Klotz,Mathew Herrnegger,Alden Keefe Sampson,Sepp Hochreiter,Grey Nearing +5 more
TL;DR: In this paper, an out-of-sample Long Short-Term Memory (LSTM) network was used for predicting in ungauged basins, where the model was trained and tested on the CAMELS basins (approximately 30 years of daily rainfall/runoff data from 531 catchments in the US of sizes ranging from 4 km to 2,000 km).
Journal ArticleDOI
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets
TL;DR: This paper proposes an adaption to the standard LSTM architecture, which it is called an Entity-Aware-L STM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model and shows that these learned caughtment similarities correspond well to what the authors would expect from prior hydrological understanding.
Journal ArticleDOI
What Role Does Hydrological Science Play in the Age of Machine Learning
Grey Nearing,Frederik Kratzert,Alden Keefe Sampson,Craig Pelissier,Daniel Klotz,Jonathan Frame,Cristina Prieto,Hoshin V. Gupta +7 more
TL;DR: This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning.
Journal ArticleDOI
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network
Martin Gauch,Martin Gauch,Frederik Kratzert,Daniel Klotz,Grey Nearing,Grey Nearing,Jimmy Lin,Sepp Hochreiter +7 more
TL;DR: This study proposes two Multi-Timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a single temporal resolution and branch out into each individual timescale for more recent input steps.