Deep Learning for Modelling of Urban Drainage Networks: A Physics-informed Surrogate Model Using Measured and Simulated Data
City-wide climate adaptation for pluvial flood mitigation requires fast and reliable simulation tools. Considering the limitations of hydrodynamic models at city-scale simulations, data driven models have high potential in the development of surrogate tools. This study explores the Google DeepMind WaveNet™ model architecture to map hydrological response of catchments onto hydraulic parameters of t