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.

借助涵洞、闸门和泵等水力结构，通过提前释放水来降低水位，有效地减轻或防止洪水的发生。本文提出了一种名为 FIDLAR 的深度学习结构，通过平衡洪水管理和预先释放的水资源浪费来实现水域洪水管理的最佳结果。通过在美国南佛罗里达水管理地区进行实验，结果表明 FIDLAR 相较于现有的先进技术在速度和水资源预放排清单上表现出更好的性能。该文的主要贡献在于有效使用模型解释工具，以理解各种环境因素对其决策的影响。