Inundation Modeling in Data Scarce Regions

Ben-Haim, Zvika, Anisimov, Vladimir, Yonas, Aaron, Gulshan, Varun, Shafi, Yusef, Hoyer, Stephan, Nevo, Sella

arXiv.org Machine Learning 

Flood forecasts are crucial for effective individual and governmental protective action. The vast majority of flood-related casualties occur in developing countries, where providing spatially accurate forecasts is a challenge due to scarcity of data and lack of funding. This paper describes an operational system providing flood extent forecast maps covering several flood-prone regions in India, with the goal of being sufficiently scalable and cost-efficient to facilitate the establishment of effective flood forecasting systems globally.

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