| 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 | 544* | 2019 |
| 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 | 131 | 2021 |
| 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 | 106 | 2022 |
| 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 | 78* | 2023 |
| Hybrid forecasting: blending climate predictions with AI models LJ Slater, L Arnal, MA Boucher, AYY Chang, S Moulds, C Murphy, ... Hydrology and earth system sciences 27 (9), 1865-1889, 2023 | 73* | 2023 |
| Modeling COVID-19 on a network: super-spreaders, testing and containment O Reich, G Shalev, T Kalvari MedRxiv, 2020.04. 30.20081828, 2020 | 36 | 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 | 31 | 2021 |
| ML for flood forecasting at scale S Nevo, V Anisimov, G Elidan, R El-Yaniv, P Giencke, Y Gigi, A Hassidim, ... arXiv preprint arXiv:1901.09583, 2019 | 31 | 2019 |
| 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 | 28* | 2024 |
| On the Fourier Entropy Influence conjecture for extremal classes G Shalev arXiv preprint arXiv:1806.03646, 2018 | 9 | 2018 |
| Accurate hydrologic modeling using less information G Shalev, R El-Yaniv, D Klotz, F Kratzert, A Metzger, S Nevo arXiv preprint arXiv:1911.09427, 2019 | 6 | 2019 |
| ML-based flood forecasting: Advances in scale, accuracy and reach S Nevo, G Elidan, A Hassidim, G Shalev, O Gilon, G Nearing, Y Matias arXiv preprint arXiv:2012.00671, 2020 | 5 | 2020 |
| Towards global remote discharge estimation: Using the few to estimate the many Y Gigi, G Elidan, A Hassidim, Y Matias, Z Moshe, S Nevo, G Shalev, ... arXiv preprint arXiv:1901.00786, 2019 | 5 | 2019 |
| Towards flood warnings everywhere-data-driven rainfall-runoff modeling at global scale F Kratzert, M Gauch, D Klotz, A Metzger, G Nearing, G Shalev, S Shenzis, ... AGU Fall Meeting Conference Abstracts, Pp. GC12A 4, 2022 | 3 | 2022 |
| Reproducing flash flood warnings with Machine Learning O Zlydenko, D Cohen, M Gauch, AG Rosenthal, F Kratzert, G Nearing, ... EGU24, 2024 | | 2024 |
| Deep Learning for Spatially Distributed Rainfall–Runoff Modeling M Gauch, F Kratzert, V Dube, O Gilon, D Klotz, A Metzger, G Nearing, ... EGU24, 2024 | | 2024 |
| GRDC-Caravan: extending the original dataset with data from the Global Runoff Data Centre C Färber, H Plessow, S Mischel, F Kratzert, N Addor, G Shalev, U Looser EGU24, 2024 | | 2024 |
| Living among Artiodactyls-Current status and future plans of the Caravan dataset F Kratzert, N Addor, G Shalev, O Gilon EGU24, 2024 | | 2024 |
| Terrestrial Information Everywhere-An AI-Based Land Surface Model S Shenzis, GS Nearing, TY Tekalign, G Shalev, O Gilon AGU23, 2023 | | 2023 |
| From Hindcast to Forecast with Deep Learning Streamflow Models G Nearing, M Gauch, D Klotz, F Kratzert, A Metzger, G Shalev, S Shenzis, ... EGU General Assembly Conference Abstracts, EGU-16974, 2023 | | 2023 |
