Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling

• URL: http://arxiv.org/abs/2106.07218v1
• Date: Mon, 14 Jun 2021 08:05:14 GMT
• Title: Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling
• Authors: Niv Giladi, Zvika Ben-Haim, Sella Nevo, Yossi Matias, Daniel Soudry
• Abstract summary: We train a deep neural network to perform physics-informed downsampling of the terrain map. We demonstrate that with this method, it is possible to achieve a significant reduction in computational cost, while maintaining an accurate solution.
• Score: 26.744689956865628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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