Search Results for author: Asher Metzger

Found 4 papers, 1 papers with code

AI Increases Global Access to Reliable Flood Forecasts

1 code implementation30 Jul 2023 Grey Nearing, Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo, Florian Pappenberger, Christel Prudhomme, Guy Shalev, Shlomo Shenzis, Tadele Tekalign, Dana Weitzner, Yoss Matias

Using AI, we achieve reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System).

Management

HydroNets: Leveraging River Structure for Hydrologic Modeling

no code implementations1 Jul 2020 Zach Moshe, Asher Metzger, Gal Elidan, Frederik Kratzert, Sella Nevo, Ran El-Yaniv

In this work we present a novel family of hydrologic models, called HydroNets, which leverages river network structure.

Management

Accurate Hydrologic Modeling Using Less Information

no code implementations21 Nov 2019 Guy Shalev, Ran El-Yaniv, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo

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.

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