Multiscale homogenized predictive modelling of flooding surface in urban cities using physics-induced deep AI with UPC

Climate change is expected to worsen streamflow conditions, in terms of their frequency and magnitude, in urbanized watersheds, which can directly result in flood scenarios of greater water surface displacements. To contribute to the scientific community's present flood mitigation and assessment activities, this study develops a generic engineering approach to construct multiscale homogenized deep neural networks (MHDNN), implemented with unified parallel C (MHDNN-UPC), by fusing mass and momentum conservation laws, as part of hydrodynamic modelling, with deep learning (DL) computations for the predictive modelling of water surface displacements due to peak streamflow conditions, as representative of flood scenarios. The approach is comprised of a series of systematic analyses, namely: (Phase A) derive homogenized effective solutions, via homogenization theory coupled with multiscale perturbation analysis for hydrodynamic modelling, to perform features extractions and constructing useful activation functions for training MHDNN predictive model(s); (Phase B) UPC implementation of MHDNN model(s) to improve their computational performance; and (Phase C) using available field datasets, pertaining to surface displacements in watersheds, to train, validate and test MHDNNUPC model(s). The proposed predictive approach is then verified across 48 selected states in the United States for modelling their recorded displacements over the past 100 years, by achieving an average 10% improvement in its predictive accuracy, as compared to other traditional models, while also improving the model's computational performance with the implemented UPC component.