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Sella Nevo
Sella Nevo
Director of the Meselson Center, RAND
Verified email at rand.org - Homepage
Title
Cited by
Cited by
Year
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
792022
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
432023
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
352020
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
302019
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
292021
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, ...
132023
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
112020
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
112019
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, ...
102023
Caravan-A global community dataset for large-sample hydrology Sci
F Kratzert, G Nearing, N Addor, T Erickson, M Gauch, O Gilon, ...
Data 10 (61), 10.1038, 2023
82023
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
62021
An inside look at flood forecasting
S Nevo
Google Al Blog, 2019
62019
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
52023
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
52020
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
52020
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
42023
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
42022
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
42019
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
42019
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
32022
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