The Power of Explainability in Forecast-Informed Deep Learning Models
  for Flood Mitigation
Jimeng Shi Vitalii Stebliankin Giri Narasimhan
Floods can cause horrific harm to life and property. However, they can be
mitigated or even avoided by the effective use of hydraulic structures such as
dams, gates, and pumps. By pre-releasing water via these structures in advance
of extreme weather events, water levels are sufficiently lowered to prevent
floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning
Architecture, achieving flood management in watersheds with hydraulic
structures in an optimal manner by balancing out flood mitigation and
unnecessary wastage of water via pre-releases. We perform experiments with
FIDLAR using data from the South Florida Water Management District, which
manages a coastal area that is highly prone to frequent storms and floods.
Results show that FIDLAR performs better than the current state-of-the-art with
several orders of magnitude speedup and with provably better pre-release
schedules. The dramatic speedups make it possible for FIDLAR to be used for
real-time flood management. The main contribution of this paper is the
effective use of tools for model explainability, allowing us to understand the
contribution of the various environmental factors towards its decisions.