  In this technical report we compare different deep learning models for
prediction of water depth rasters at high spatial resolution. Efficient,
accurate, and fast methods for water depth prediction are nowadays important as
urban floods are increasing due to higher rainfall intensity caused by climate
change, expansion of cities and changes in land use. While hydrodynamic models
models can provide reliable forecasts by simulating water depth at every
location of a catchment, they also have a high computational burden which
jeopardizes their application to real-time prediction in large urban areas at
high spatial resolution. Here, we propose to address this issue by using
data-driven techniques. Specifically, we evaluate deep learning models which
are trained to reproduce the data simulated by the CADDIES cellular-automata
flood model, providing flood forecasts that can occur at different future time
horizons. The advantage of using such models is that they can learn the
underlying physical phenomena a priori, preventing manual parameter setting and
computational burden. We perform experiments on a dataset consisting of two
catchments areas within Switzerland with 18 simpler, short rainfall patterns
and 4 long, more complex ones. Our results show that the deep learning models
present in general lower errors compared to the other methods, especially for
water depths $>0.5m$. However, when testing on more complex rainfall events or
unseen catchment areas, the deep models do not show benefits over the simpler
ones.
