Summary of High-resolution Flood Probability Mapping Using Generative Machine Learning with Large-scale Synthetic Precipitation and Inundation Data, by Lipai Huang et al.
High-Resolution Flood Probability Mapping Using Generative Machine Learning with Large-Scale Synthetic Precipitation and Inundation Data
by Lipai Huang, Federico Antolini, Ali Mostafavi, Russell Blessing, Matthew Garcia, Samuel D. Brody
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This study introduces Precipitation-Flood Depth Generative Pipeline, a novel methodology that leverages generative machine learning to generate large-scale synthetic inundation data and produce probabilistic flood maps. The pipeline begins by training a cell-wise depth estimator using precipitation-flood events modeled with a physics-based approach, which outperforms universal models. Next, the Conditional Generative Adversarial Network (CGAN) is used to conditionally generate synthetic precipitation point cloud, filtered to align with realistic patterns. This enables strategic sampling and generation of synthetic precipitation events. The pipeline generates 10,000 synthetic events and creates flood probability maps for various inundation depths. Validation using similarity and correlation metrics confirms the accuracy of the synthetic depth distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps create detailed maps that show where flooding is likely to happen in the future. Right now, making these maps is hard because it takes a lot of time and computing power. The researchers created a new way to make these maps using machine learning. They started by training a model that can predict how deep floodwaters will be based on rain data. Then they used this model to generate lots of fake rain data that looks like real rain data. This fake data helped them create maps that show the likelihood of flooding at different depths. The new method is fast and efficient, making it useful for creating detailed flood maps. |
Keywords
» Artificial intelligence » Generative adversarial network » Likelihood » Machine learning » Probability
