Towards AI for approximating hydrodynamic simulations as a 2D segmentation task

Lydia Bryan-Smith, Nina Dethlefs

Keywords: image segmentation, semantic segmentation, simulations, flooding

TL;DR: Image semantic segmentation is applied in a proof of concept to approximate a physics-based simulation to predict water depth during flooding events.

Abstract: Traditional predictive simulations and remote sensing techniques for forecasting floods are based on fixed and spatially restricted physics-based models. These models are computationally expensive and can take many hours to run, resulting in predictions made based on outdated data. They are also spatially fixed, and unable to scale to unknown areas. By modelling the task as an image segmentation problem, an alternative approach using artificial intelligence to approximate the parameters of a physics-based model in 2D is demonstrated, enabling rapid predictions to be made in real-time.

Git: https://github.com/sbrl/research-rainfallradar

Project: https://github.com/sbrl/research-rainfallradar

Submission Number: 2