Low-lying coastal cities, exemplified by Norfolk, Virginia, face the
challenge of street flooding caused by rainfall and tides, which strain
transportation and sewer systems and can lead to property damage. While
high-fidelity, physics-based simulations provide accurate predictions of urban
pluvial flooding, their computational complexity renders them unsuitable for
real-time applications. Using data from Norfolk rainfall events between 2016
and 2018, this study compares the performance of a previous surrogate model
based on a random forest algorithm with two deep learning models: Long
Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This investigation
underscores the importance of using a model architecture that supports the
communication of prediction uncertainty and the effective integration of
relevant, multi-modal features.

通过 2016 年至 2018 年诺福克的降雨事件数据，本研究比较了基于随机森林算法的以前的代理模型与两种深度学习模型（LSTM 和 GRU）的性能，突出了使用支持预测不确定性传达和有效整合相关多模态特征的模型架构的重要性。