Offer Description
The project aims to develop and validate Artificial Intelligence-based surrogate models for the rapid mapping of flooding resulting from levee breaches. Starting from high-resolution 2D hydrodynamic simulations, representative datasets will be built, covering different breach scenarios, hydrological conditions, and morphological characteristics of the territory. These data will be used to train machine learning algorithms capable of reproducing, with reduced computational time, the main hydraulic variables of interest, such as flood extent, water depths, and flood wave arrival times. The expected results will contribute to the development of operational tools for flood risk management, support for emergency planning, and the rapid assessment of hazard scenarios in levee-protected areas.
Where to apply
Website: https://www.unipr.it/
