Summary of Large-scale Flood Modeling and Forecasting with Floodcast, by Qingsong Xu et al.
Large-scale flood modeling and forecasting with FloodCast
by Qingsong Xu, Yilei Shi, Jonathan Bamber, Chaojun Ouyang, Xiao Xiang Zhu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 The proposed FloodCast framework aims to improve large-scale flood forecasting by leveraging multi-satellite observations and a geometry-adaptive physics-informed neural solver (GeoPINS). The framework consists of two modules: multi-satellite observation and hydrodynamic modeling. The unsupervised change detection method and rainfall processing tool in the observation module utilize real-time satellite data to enhance flood prediction accuracy. GeoPINS, introduced as a fast, accurate, and resolution-invariant architecture with Fourier neural operators, demonstrates impressive performance on popular PDEs across regular and irregular domains. Building upon GeoPINS, a sequence-to-sequence model is proposed to handle long-term temporal series and extensive spatial domains in large-scale flood modeling. The framework’s performance is validated using a benchmark dataset from the 2022 Pakistan flood, demonstrating exceptional agreement with traditional hydrodynamics during high water levels and outperforming it when compared to SAR-based flood depth data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Flood forecasting helps save lives by predicting when floods will happen and how severe they’ll be. Right now, big computer models that try to predict floods are limited because they use fixed-size grids and take a long time to run. This makes it hard for them to accurately forecast flood crests and issue timely warnings. A team of researchers has created a new framework called FloodCast that can predict floods more accurately and quickly by using data from multiple satellites and a special kind of computer model called GeoPINS. The team tested their approach on real-world data from the 2022 Pakistan flood and found it worked well. |
Keywords
* Artificial intelligence * Sequence model * Unsupervised
