| Rainfall-runoff modelling using long short-term memory (lstm) networks F Kratzert, D Klotz, C Brenner, K Schulz, M Herrnegger Hydrol. Earth Syst. Sci. 22, 6005-6022, 2018 | 1080 | 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 | 531* | 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 | 428 | 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 | 338 | 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 | 156 | 2021 |
| Deep learning rainfall-runoff predictions of extreme events J Frame, F Kratzert, D Klotz, M Gauch, G Shelev, O Gilon, LM Qualls, ... Copernicus GmbH, 2021 | 121 | 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 | 110 | 2019 |
| Post‐processing the national water model with long short‐term memory networks for streamflow predictions and model diagnostics JM Frame, F Kratzert, A Raney, M Rahman, FR Salas, GS Nearing JAWRA Journal of the American Water Resources Association 57 (6), 885-905, 2021 | 108* | 2021 |
| Uncertainty estimation with deep learning for rainfall–runoff modeling D Klotz, F Kratzert, M Gauch, A Keefe Sampson, J Brandstetter, ... Hydrology and Earth System Sciences 26 (6), 1673-1693, 2022 | 99 | 2022 |
| Flood forecasting with machine learning models in an operational framework S Nevo, E Morin, A Gerzi Rosenthal, A Metzger, C Barshai, D Weitzner, ... Hydrology and Earth System Sciences 26 (15), 4013-4032, 2022 | 97 | 2022 |
| 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 | 83 | 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 | 74 | 2021 |
| 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 | 70 | 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 | 52 | 2023 |
| 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 | 47 | 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 | 39 | 2022 |
| Hydronets: Leveraging river structure for hydrologic modeling Z Moshe, A Metzger, G Elidan, F Kratzert, S Nevo, R El-Yaniv arXiv preprint arXiv:2007.00595, 2020 | 38 | 2020 |
| 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 | 30 | 2021 |
| On strictly enforced mass conservation constraints for modelling the rainfall‐runoff process JM Frame, F Kratzert, HV Gupta, P Ullrich, GS Nearing Hydrological Processes 37 (3), e14847, 2023 | 23 | 2023 |
| Fish species classification in underwater video monitoring using Convolutional Neural Networks F Kratzert, H Mader EarthArXiv, 2018 | 22 | 2018 |
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