  Timely, accurate, and reliable information is essential for decision-makers,
emergency managers, and infrastructure operators during flood events. This
study demonstrates a proposed machine learning model, MaxFloodCast, trained on
physics-based hydrodynamic simulations in Harris County, offers efficient and
interpretable flood inundation depth predictions. Achieving an average
R-squared of 0.949 and a Root Mean Square Error of 0.61 ft on unseen data, it
proves reliable in forecasting peak flood inundation depths. Validated against
Hurricane Harvey and Storm Imelda, MaxFloodCast shows the potential in
supporting near-time floodplain management and emergency operations. The
model's interpretability aids decision-makers in offering critical information
to inform flood mitigation strategies, to prioritize areas with critical
facilities and to examine how rainfall in other watersheds influences flood
exposure in one area. The MaxFloodCast model enables accurate and interpretable
inundation depth predictions while significantly reducing computational time,
thereby supporting emergency response efforts and flood risk management more
effectively.
