ML for Flood Forecasting at Scale

Sella Nevo
Sella Nevo
Vova Anisimov
Vova Anisimov
Pete Giencke
Pete Giencke
Yotam Gigi
Yotam Gigi
Zach Moshe
Zach Moshe
Mor Schlesinger
Mor Schlesinger
Guy Shalev
Guy Shalev
Ajai Tirumali
Ajai Tirumali

arXiv: Learning, 2018.

Cited by: 1|Bibtex|Views25
EI
Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

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 of data for a specific location, and the computational difficulty of building continent/global level models that are sufficiently accurate. Machine learning (ML)...More

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