39 citations · 102 across the 14 of their papers we have counts for
5 papers · 1 filter
Accurate Hydrologic Modeling Using Less Information
Guy Shalev, Ran El-Yaniv, Daniel Klotz +3
Joint models are a common and important tool in the intersection of machine learning and the physical sciences, particularly in contexts where real-world measurements are scarce. R…
Inundation Modeling in Data Scarce Regions
Zvika Ben-Haim, Vladimir Anisimov, Aaron Yonas +4
Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providin…
Spectral Algorithm for Low-rank Multitask Regression
Yotam Gigi, Ami Wiesel, Sella Nevo +3
Multitask learning, i.e. taking advantage of the relatedness of individual tasks in order to improve performance on all of them, is a core challenge in the field of machine learnin…
ML for Flood Forecasting at Scale
Sella Nevo, Vova Anisimov, Gal Elidan +11
Effective riverine flood forecasting at scale is hindered by a multitude of factors, most notably the need to rely on human calibration in current methodology, the limited amount o…
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Yotam Gigi, Gal Elidan, Avinatan Hassidim +5
Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river disc…