ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach

Sella Nevo, Gal Elidan, Avinatan Hassidim, Guy Shalev, Oren Gilon, Grey Nearing, Yossi Matias

Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems, due to core challenges in scalability, computational costs, and data availability. In this paper we present two components of flood forecasting systems which were developed over the past year, providing access to these critical systems to 75 million people who didn't have this access before.

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