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
this https URL

在沿海河流系统中，通过使用诸如水坝、闸门、泵和水库等水力结构，以预测前释放水来减轻或甚至预防在暴风雨或大潮期间频繁发生的洪水，可以在很大程度上降低生命和财产的威胁。本文提出了一种名为 FIDLAR 的预测通知深度学习架构，用于实现快速和最优的洪水管理以及准确的预释放水。FIDLAR 无缝集成两个神经网络模块：Flood Manager 负责生成水预释放计划，Flood Evaluator 评估这些生成的计划。以梯度反馈的方式使用 Evaluator 模块进行单独预训练，以训练 Manager 模型以确保最优的水预释放固定计划。我们使用南佛罗里达易受频繁暴风雨影响的洪水易发沿海地区的数据对 FIDLAR 进行了实验。结果显示，FIDLAR 比当前使用的基于物理模型的方法快几个数量级，并通过改进的水预释放计划胜过了基准方法。我们的代码在此 https URL。