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。

诺福克弗吉尼亚市街道尺度洪水的机器学习替代模型比较

A comparison of machine learning surrogate models of street-scale  flooding in Norfolk, Virginia

Effective environmental planning and management to address climate change
could be achieved through extensive environmental modeling with machine
learning and conventional physical models. In order to develop and improve
these models, practitioners and researchers need comprehensive benchmark
datasets that are prepared and processed with environmental expertise that they
can rely on. This study presents an extensive dataset of rainfall events for
the state of Iowa (2016-2019) acquired from the National Weather Service Next
Generation Weather Radar (NEXRAD) system and processed by a quantitative
precipitation estimation system. The dataset presented in this study could be
used for better disaster monitoring, response and recovery by paving the way
for both predictive and prescriptive modeling.

本研究提供了 2016 年至 2019 年间爱荷华州降雨事件的一个庞大而全面的数据集，该数据集可用于实现环境规划和气候变化建模。