Title: ML for Flood Forecasting at Scale,
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) is primed to be useful in this
scenario: learned models often surpass human experts in complex
high-dimensional scenarios, and the framework of transfer or multitask learning
is an appealing solution for leveraging local signals to achieve improved
global performance. We propose to build on these strengths and develop ML
systems for timely and accurate riverine flood prediction.