Physics-Aware Downsampling with Deep Learning for Scalable Flood ModelingDownload PDF

21 May 2021, 20:49 (edited 23 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Partial differential equation, Inundation modeling, Physics-informed, Downsampling, Flood modeling
  • TL;DR: Finding an optimized coarse grid representation of terrain maps with a physics aware deep neural network, for accurate flood prediction.
  • Abstract: Background. Floods are the most common natural disaster in the world, affecting the lives of hundreds of millions. Flood forecasting is therefore a vitally important endeavor, typically achieved using physical water flow simulations, which rely on accurate terrain elevation maps. However, such simulations, based on solving partial differential equations, are computationally prohibitive on a large scale. This scalability issue is commonly alleviated using a coarse grid representation of the elevation map, though this representation may distort crucial terrain details, leading to significant inaccuracies in the simulation.\\ Contributions. We train a deep neural network to perform physics-informed downsampling of the terrain map: we optimize the coarse grid representation of the terrain maps, so that the flood prediction will match the fine grid solution. For the learning process to succeed, we configure a dataset specifically for this task. We demonstrate that with this method, it is possible to achieve a significant reduction in computational cost, while maintaining an accurate solution. A reference implementation accompanies the paper as well as documentation and code for dataset reproduction.
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