Deep Neural Networks with 3D Point Clouds for Empirical Friction
  Measurements in Hydrodynamic Flood Models
Francisco Haces-Garcia Vasileios Kotzamanis Craig Glennie Hanadi Rifai
Friction is one of the cruxes of hydrodynamic modeling; flood conditions are
highly sensitive to the Friction Factors (FFs) used to calculate momentum
losses. However, empirical FFs are challenging to measure because they require
laboratory experiments. Flood models often rely on surrogate observations (such
as land use) to estimate FFs, introducing uncertainty. This research presents a
laboratory-trained Deep Neural Network (DNN), trained using flume experiments
with data augmentation techniques, to measure Manning's n based on Point Cloud
data. The DNN was deployed on real-world lidar Point Clouds to directly measure
Manning's n under regulatory and extreme storm events, showing improved
prediction capabilities in both 1D and 2D hydrodynamic models. For 1D models,
the lidar values decreased differences with regulatory models for in-channel
water depth when compared to land cover values. For 1D/2D coupled models, the
lidar values produced better agreement with flood extents measured from
airborne imagery, while better matching flood insurance claim data for
Hurricane Harvey. In both 1D and 1D/2D coupled models, lidar resulted in better
agreement with validation gauges. For these reasons, the lidar measurements of
Manning's n were found to improve both regulatory models and forecasts for
extreme storm events, while simultaneously providing a pathway to standardize
the measurement of FFs. Changing FFs significantly affected fluvial and pluvial
flood models, while surge flooding was generally unaffected. Downstream flow
conditions were found to change the importance of FFs to fluvial models,
advancing the literature of friction in flood models. This research introduces
a reliable, repeatable, and readily-accessible avenue to measure
high-resolution FFs based on 3D point clouds, improving flood prediction, and
removing uncertainty from hydrodynamic modeling.