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.

翻译：洪水会对生命和财产造成严重危害。然而，通过有效利用水坝、闸门和水泵等水工结构，可以缓解甚至避免洪水灾害。通过在极端天气事件来临前利用这些结构提前泄水，可充分降低水位以防洪。本文提出了一种名为FIDLAR的预报信息深度学习架构，通过平衡防洪减灾与预泄造成的不必要水资源浪费，以最优方式实现流域内水工结构的洪水管理。我们利用南佛罗里达水资源管理区的数据对FIDLAR进行了实验，该区域管理着极易遭受频繁风暴和洪水侵袭的沿海地区。结果表明，FIDLAR的性能优于现有最先进方法，速度提升数个数量级，且可证明其预泄调度方案更优。显著的加速优势使FIDLAR能够用于实时洪水管理。本文的主要贡献在于有效运用模型可解释性工具，从而理解不同环境因素对其决策的贡献。