Summary of Deep Neural Networks with 3d Point Clouds For Empirical Friction Measurements in Hydrodynamic Flood Models, by Francisco Haces-garcia et al.
Deep Neural Networks with 3D Point Clouds for Empirical Friction Measurements in Hydrodynamic Flood Models
by Francisco Haces-Garcia, Vasileios Kotzamanis, Craig Glennie, Hanadi Rifai
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Fluid Dynamics (physics.flu-dyn)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning approach is presented for accurately measuring Manning’s n, a crucial friction factor in hydrodynamic modeling. The research leverages laboratory-trained Deep Neural Networks (DNNs) trained using flume experiments with data augmentation techniques to estimate Manning’s n from Point Cloud data. The DNN is deployed on real-world lidar Point Clouds to directly measure Manning’s n under regulatory and extreme storm events, demonstrating improved prediction capabilities in both 1D and 2D hydrodynamic models. The study highlights the importance of accurate friction factor measurements for predicting flood conditions, showcasing the potential for standardized FF measurement using lidar-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict how water will move during a big storm or flood. Scientists have been struggling to get this right because they need special equipment to measure something called “friction” that affects how the water moves. This research uses special computers and data from laser scans to measure friction more accurately than before. By doing so, it can improve predictions of where the water will go during a storm or flood. This is important because it can help save lives and property by giving emergency responders better information. |
Keywords
* Artificial intelligence * Data augmentation * Deep learning
