Assessing flood resilience in urban drainage networks using deep learning–based hydraulic predictions

As urban flood risks intensify due to climate change and rapid urbanization, robust assessment of drainage system resilience has become increasingly important. This study proposes a deep learning–based framework to evaluate flood resilience in urban drainage networks (UDNs) using node-level hydraulic predictions. The framework integrates Graph Neural Networks (GNNs) and Transformer models to predict water depth at each network node under multiple storm scenarios. GNNs capture spatial dependencies and network topology within the drainage system, while the Transformer models temporal rainfall–runoff dynamics. Flooding conditions at nodes are identified by applying depth-based thresholds to the predicted water levels, enabling the generation of time-resolved flood maps across the network. Flood resilience is assessed at the node level by adapting the Simple Urban Flood Resilience Index (SUFRI). Three indicators are considered: normalized flood depth at nodes, recovery time required for water levels to return to normal conditions, and flooding frequency. These indicators are combined to derive resilience scores for individual nodes, which are further weighted according to their hydraulic and topological importance within the network, considering factors such as flow capacity, connectivity, and redundancy. System-level resilience is obtained by aggregating the weighted node-level resilience scores. The proposed framework is applied to a real-world urban drainage system to evaluate resilience under diverse storm scenarios. Results reveal critical nodes and vulnerable regions that disproportionately influence overall system performance. Based on the analysis, targeted optimization strategies—such as capacity enhancement, redundancy improvement, and recovery acceleration—are suggested to mitigate future flood risks. The framework provides a scalable and data-efficient decision-support tool for urban flood resilience assessment and infrastructure planning, particularly in data-scarce urban environments.

Acknowledgement
This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and Technology (RS-2024-00356786) and Korea Environmental Industry & Technology Institute grant funded by the Ministry of Environment (RS-2023-00218973).