Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks

arXiv:2404.10324v1 Announce Type: new
Abstract: Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. Owing to the state-of-the-art modelling power of graph neural networks (GNNs) and their match with urban drainage networks in the graph structure, this work proposes a GNN-based surrogate of …

abstract arxiv cs.ce cs.lg cs.sy eess.sy efficiency graph graph neural network interpretability low modelling network networks neural network neural networks nns physics prediction predictive real-time type urban