  Flooding is a destructive and dangerous hazard and climate change appears to
be increasing the frequency of catastrophic flooding events around the world.
Physics-based flood models are costly to calibrate and are rarely generalizable
across different river basins, as model outputs are sensitive to site-specific
parameters and human-regulated infrastructure. In contrast, statistical models
implicitly account for such factors through the data on which they are trained.
Such models trained primarily from remotely-sensed Earth observation data could
reduce the need for extensive in-situ measurements. In this work, we develop
generalizable, multi-basin models of river flooding susceptibility using
geographically-distributed data from the USGS stream gauge network. Machine
learning models are trained in a supervised framework to predict two measures
of flood susceptibility from a mix of river basin attributes, impervious
surface cover information derived from satellite imagery, and historical
records of rainfall and stream height. We report prediction performance of
multiple models using precision-recall curves, and compare with performance of
naive baselines. This work on multi-basin flood prediction represents a step in
the direction of making flood prediction accessible to all at-risk communities.
