| 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 | 61 | 2023 |
| 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 | 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 | 30 | 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 | 20 | 2024 |
| Caravan–A global community dataset for large-sample hydrology, Sci. Data, 10, 61 F Kratzert, G Nearing, N Addor, T Erickson, M Gauch, O Gilon, ... | 18 | 2023 |
| A deep learning architecture for conservative dynamical systems: Application to rainfall-runoff modeling G Nearing, F Kratzert, D Klotz, PJ Hoedt, G Klambauer, S Hochreiter, ... AI for Earth Sciences Workshop at NeurIPS, 2020 | 14 | 2020 |
| Caravan-A global community dataset for large-sample hydrology, Scientific Data, 10, 61 F Kratzert, G Nearing, N Addor, T Erickson, M Gauch, O Gilon, ... | 12 | 2023 |
| Inundation modeling in data scarce regions Z Ben-Haim, V Anisimov, A Yonas, V Gulshan, Y Shafi, S Hoyer, S Nevo arXiv preprint arXiv:1910.05006, 2019 | 11 | 2019 |
| AI increases global access to reliable flood forecasts G Nearing, D Cohen, V Dube, M Gauch, O Gilon, S Harrigan, A Hassidim, ... arXiv preprint arXiv:2307.16104, 2023 | 8 | 2023 |
| Physics-aware downsampling with deep learning for scalable flood modeling N Giladi, Z Ben-Haim, S Nevo, Y Matias, D Soudry Advances in Neural Information Processing Systems 34, 1378-1389, 2021 | 7 | 2021 |
| A neural encoder for earthquake rate forecasting O Zlydenko, G Elidan, A Hassidim, D Kukliansky, Y Matias, B Meade, ... Scientific Reports 13 (1), 12350, 2023 | 6 | 2023 |
| 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 |
| An inside look at flood forecasting S Nevo Google Al Blog, 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 |
| Hydronets: Leveraging river structure for hydrologic modeling, arXiv Z Moshe, A Metzger, G Elidan, F Kratzert, S Nevo, R El-Yaniv arXiv preprint arXiv:2007.00595 1, 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 |
| Securing Artificial Intelligence Model Weights: Interim Report S Nevo, D Lahav, A Karpur, J Alstott, J Matheny RAND, 2023 | 4 | 2023 |
| Accelerating physics simulations with tensor processing units: An inundation modeling example RL Hu, D Pierce, Y Shafi, A Boral, V Anisimov, S Nevo, Y Chen The International Journal of High Performance Computing Applications 36 (4 …, 2022 | 4 | 2022 |
