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。

FIDLAR：用于洪灾缓解的预测引导深度学习架构

FIDLAR: Forecast-Informed Deep Learning Architecture for Flood  Mitigation

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。