  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.

Machine Learning;Computer Vision and Pattern Recognition