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.

这项研究通过使用数据增强技术对模拟实验进行训练，利用点云数据开发出一种基于深度神经网络的实验室训练模型，能够直接测量 Manning's n 值，并在规范性和极端气象事件下的 1D 和 2D 水动力模型中展现了改进的预测能力，该研究提供了一种可靠、可重复和易获取的测量摩擦因子的方法，从而提高洪水预测精度并减少水动力建模中的不确定性。

基于三维点云的深度神经网络用于水动力洪水模型中的摩擦实验测量

Deep Neural Networks with 3D Point Clouds for Empirical Friction  Measurements in Hydrodynamic Flood Models

Large-scale hydrodynamic models generally rely on fixed-resolution spatial
grids and model parameters as well as incurring a high computational cost. This
limits their ability to accurately forecast flood crests and issue
time-critical hazard warnings. In this work, we build a fast, stable, accurate,
resolution-invariant, and geometry-adaptative flood modeling and forecasting
framework that can perform at large scales, namely FloodCast. The framework
comprises two main modules: multi-satellite observation and hydrodynamic
modeling. In the multi-satellite observation module, a real-time unsupervised
change detection method and a rainfall processing and analysis tool are
proposed to harness the full potential of multi-satellite observations in
large-scale flood prediction. In the hydrodynamic modeling module, a
geometry-adaptive physics-informed neural solver (GeoPINS) is introduced,
benefiting from the absence of a requirement for training data in
physics-informed neural networks and featuring a fast, accurate, and
resolution-invariant architecture with Fourier neural operators. GeoPINS
demonstrates impressive performance on popular PDEs across regular and
irregular domains. Building upon GeoPINS, we propose a sequence-to-sequence
GeoPINS model to handle long-term temporal series and extensive spatial domains
in large-scale flood modeling. Next, we establish a benchmark dataset in the
2022 Pakistan flood to assess various flood prediction methods. Finally, we
validate the model in three dimensions - flood inundation range, depth, and
transferability of spatiotemporal downscaling. Traditional hydrodynamics and
sequence-to-sequence GeoPINS exhibit exceptional agreement during high water
levels, while comparative assessments with SAR-based flood depth data show that
sequence-to-sequence GeoPINS outperforms traditional hydrodynamics, with
smaller prediction errors.

建立了一种快速、稳定、准确、分辨率无关和几何自适应的洪水建模和预测框架，包括多卫星观测和水动力建模模块，以及基于几何自适应的物理信息神经求解器（GeoPINS），并通过与传统水动力学和基于合成孔径雷达的洪水深度数据的比较验证模型的性能。