Daniel Klotz

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Coauteurs

Frederik KratzertResearch Scientist @ GoogleAdresse e-mail validée de google.com
Grey NearingGoogle ResearchAdresse e-mail validée de google.com
Sepp HochreiterInstitute for Machine Learning, Johannes Kepler University LinzAdresse e-mail validée de ml.jku.at
Martin GauchGoogleAdresse e-mail validée de google.com
Mathew HerrneggerUniversity of Natural Resources and Life Sciences, Vienna, Institute of Hydrology and WaterAdresse e-mail validée de boku.ac.at
Karsten SchulzUniversity of Natural Resources and Life Sciences (BOKU) ViennaAdresse e-mail validée de boku.ac.at
Günter KlambauerProf., LIT AI Lab & Institute for Machine Learning, Johannes Kepler UniversityAdresse e-mail validée de ml.jku.at
Guy ShalevGoogleAdresse e-mail validée de google.com
Oren GilonGoogle ResearchAdresse e-mail validée de google.com
Sella NevoDirector of the Meselson Center, RANDAdresse e-mail validée de rand.org
Johannes BrandstetterJohannes Kepler University (JKU) LinzAdresse e-mail validée de ml.jku.at
Claire BrennerPhD cand. at Institute of Hydrology and Water Management, University of Natural Resources and Life SciencesAdresse e-mail validée de boku.ac.at
Jimmy LinUniversity of WaterlooAdresse e-mail validée de uwaterloo.ca
Yossi MatiasGoogleAdresse e-mail validée de tau.ac.il
Pieter-Jan HoedtInstitute for Machine Learning, Johannes Kepler University LinzAdresse e-mail validée de ml.jku.at
Moritz FeiglCo-founder, baseflow AI solutionsAdresse e-mail validée de baseflow.ai
Thomas LeesUniversity of OxfordAdresse e-mail validée de chch.ox.ac.uk
Louise SlaterProfessor of Hydroclimatology, University of OxfordAdresse e-mail validée de ouce.ox.ac.uk
Simon DadsonUniversity of Oxford, UKCEHAdresse e-mail validée de ouce.ox.ac.uk
Johannes WesemannInstitute for Hydrology and Water Management, Universität für Bodenkultur WienAdresse e-mail validée de boku.ac.at

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Daniel Klotz

IT:U; Google

Adresse e-mail validée de it-u.at

Earth Science Hydrology Time Series Machine Learning Imperfect Fits


Titre Trier par citations Trier par année Trier par titre	Citée par Citée par	Année
Rainfall–runoff modelling using long short-term memory (LSTM) networks F Kratzert, D Klotz, C Brenner, K Schulz, M Herrnegger Hydrology and Earth System Sciences 22 (11), 6005-6022, 2018	1342	2018
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets F Kratzert, D Klotz, G Shalev, G Klambauer, S Hochreiter, G Nearing Hydrology and Earth System Sciences 23 (12), 5089-5110, 2019	675*	2019
Toward improved predictions in ungauged basins: Exploiting the power of machine learning F Kratzert, D Klotz, M Herrnegger, AK Sampson, S Hochreiter, GS Nearing Water Resources Research 55 (12), 11344-11354, 2019	549	2019
What role does hydrological science play in the age of machine learning? GS Nearing, F Kratzert, AK Sampson, CS Pelissier, D Klotz, JM Frame, ... Water Resources Research 57 (3), e2020WR028091, 2021	466	2021
Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network M Gauch, F Kratzert, D Klotz, G Nearing, J Lin, S Hochreiter Hydrology and Earth System Sciences 25 (4), 2045-2062, 2021	208	2021
Deep learning rainfall-runoff predictions of extreme events J Frame, F Kratzert, D Klotz, M Gauch, G Shelev, O Gilon, LM Qualls, ... (No Title), 2021	185	2021
Uncertainty estimation with deep learning for rainfall–runoff modelling D Klotz, F Kratzert, M Gauch, A Keefe Sampson, J Brandstetter, ... Hydrology and Earth System Sciences Discussions 2021, 1-32, 2021	131	2021
NeuralHydrology–interpreting LSTMs in hydrology F Kratzert, M Herrnegger, D Klotz, S Hochreiter, G Klambauer Explainable AI: Interpreting, explaining and visualizing deep learning, 347-362, 2019	129	2019
Hydrological concept formation inside long short-term memory (LSTM) networks T Lees, S Reece, F Kratzert, D Klotz, M Gauch, J De Bruijn, R Kumar Sahu, ... Hydrology and Earth System Sciences Discussions 2021, 1-37, 2021	107	2021
Caravan-A global community dataset for large-sample hydrology F Kratzert, G Nearing, N Addor, T Erickson, M Gauch, O Gilon, ... Scientific Data 10 (1), 61, 2023	105	2023
Mc-lstm: Mass-conserving lstm PJ Hoedt, F Kratzert, D Klotz, C Halmich, M Holzleitner, GS Nearing, ... International conference on machine learning, 4275-4286, 2021	94	2021
A note on leveraging synergy in multiple meteorological data sets with deep learning for rainfall–runoff modeling F Kratzert, D Klotz, S Hochreiter, GS Nearing Hydrology and Earth System Sciences 25 (5), 2685-2703, 2021	89	2021
Global prediction of extreme floods in ungauged watersheds G Nearing, D Cohen, V Dube, M Gauch, O Gilon, S Harrigan, A Hassidim, ... Nature 627 (8004), 559-563, 2024	83*	2024
The great lakes runoff intercomparison project phase 4: the great lakes (GRIP-GL) J Mai, H Shen, BA Tolson, É Gaborit, R Arsenault, JR Craig, V Fortin, ... Hydrology and Earth System Sciences 26 (13), 3537-3572, 2022	68	2022
NeuralHydrology---A Python library for Deep Learning research in hydrology F Kratzert, M Gauch, G Nearing, D Klotz Journal of Open Source Software 7 (71), 4050, 2022	62	2022
HESS Opinions: Never train an LSTM on a single basin F Kratzert, M Gauch, D Klotz, G Nearing Hydrology and Earth system sciences discussions 2024, 1-19, 2024	50*	2024
Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks GS Nearing, D Klotz, AK Sampson, F Kratzert, M Gauch, JM Frame, ... Hydrology and earth system sciences discussions 2021, 1-25, 2021	39	2021
Symbolic regression for the estimation of transfer functions of hydrological models D Klotz, M Herrnegger, K Schulz Water Resources Research 53 (11), 9402-9423, 2017	32	2017
Function space optimization: A symbolic regression method for estimating parameter transfer functions for hydrological models M Feigl, M Herrnegger, D Klotz, K Schulz Water resources research 56 (10), e2020WR027385, 2020	28	2020
Conformal prediction for time series with modern hopfield networks A Auer, M Gauch, D Klotz, S Hochreiter Advances in Neural Information Processing Systems 36, 56027-56074, 2023	26	2023

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