  In coastal river systems, frequent floods, often occurring during major
storms or king tides, pose a severe threat to lives and property. However,
these floods can be mitigated or even prevented by strategically releasing
water before extreme weather events with hydraulic structures such as dams,
gates, pumps, and reservoirs. A standard approach used by local water
management agencies is the "rule-based" method, which specifies predetermined
pre-releases of water based on historical and time-tested human experience, but
which tends to result in excess or inadequate water release. The model
predictive control (MPC), a physics-based model for prediction, is an
alternative approach, albeit involving computationally intensive calculations.
In this paper, we propose a Forecast Informed Deep Learning Architecture,
FIDLAR, to achieve rapid and optimal flood management with precise water
pre-releases. FIDLAR seamlessly integrates two neural network modules: one
called the Flood Manager, which is responsible for generating water pre-release
schedules, and another called the Flood Evaluator, which assesses these
generated schedules. The Evaluator module is pre-trained separately, and its
gradient-based feedback is used to train the Manager model, ensuring optimal
water pre-releases. We have conducted experiments using FIDLAR with data from a
flood-prone coastal area in South Florida, particularly susceptible to frequent
storms. Results show that FIDLAR is several orders of magnitude faster than
currently used physics-based approaches while outperforming baseline methods
with improved water pre-release schedules. Our code is at
https://github.com/JimengShi/FIDLAR/.
