A comparison of machine learning surrogate models of street-scale
  flooding in Norfolk, Virginia
Diana McSpadden Steven Goldenberg Binata Roy Malachi Schram Jonathan L. Goodall Heather Richter
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.