Flood forecasting with machine learning models in an operational framework

no code implementations • 4 Nov 2021 • Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, Yossi Matias

During the 2021 monsoon season, the flood warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area of 287, 000 km2, home to more than 350M people.

BIG-bench Machine Learning

Spectral Algorithm for Low-rank Multitask Regression

no code implementations • 27 Oct 2019 • Yotam Gigi, Ami Wiesel, Sella Nevo, Gal Elidan, Avinatan Hassidim, Yossi Matias

In this scenario sharing a low-rank component between the tasks translates to a shared spectral reflection of the water, which is a true underlying physical model.

Image Classification regression

ML for Flood Forecasting at Scale

no code implementations • 28 Jan 2019 • Sella Nevo, Vova Anisimov, Gal Elidan, Ran El-Yaniv, Pete Giencke, Yotam Gigi, Avinatan Hassidim, Zach Moshe, Mor Schlesinger, Guy Shalev, Ajai Tirumali, Ami Wiesel, Oleg Zlydenko, Yossi Matias

We propose to build on these strengths and develop ML systems for timely and accurate riverine flood prediction.

Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many

no code implementations • 3 Jan 2019 • Yotam Gigi, Gal Elidan, Avinatan Hassidim, Yossi Matias, Zach Moshe, Sella Nevo, Guy Shalev, Ami Wiesel

We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.