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

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Francisco Haces‐Garcia , Vasileios Kotzamanis , Craig Glennie , Hanadi S. Rifai · YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2404.02234

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.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2404.02234

PDF

https://arxiv.org/pdf/2404.02234

OA Status

green

Cited By

1

Related Works

10

OpenAlex ID

https://openalex.org/W4393967877

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OpenAlex ID

https://openalex.org/W4393967877

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DOI

https://doi.org/10.48550/arxiv.2404.02234

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Title

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

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Type

preprint

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Language

en

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Publication year

2024

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Publication date

2024-04-02

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Authors

Francisco Haces‐Garcia, Vasileios Kotzamanis, Craig Glennie, Hanadi S. Rifai

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Landing page

https://arxiv.org/abs/2404.02234

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PDF URL

https://arxiv.org/pdf/2404.02234

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2404.02234

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Concepts

Flood myth, Point cloud, Artificial neural network, Point (geometry), Deep neural networks, Geology, Computer science, Artificial intelligence, Geography, Mathematics, Geometry, Archaeology

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Cited by

1

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2024: 1

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Related works (count)

10

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