An exposure-aware graph neural network for peak flood depth prediction in New Jersey urban watersheds

Abstract

Study region This study evaluates flood prediction for three northern New Jersey watersheds. The municipalities, Englewood, Irvington, and Paterson, New Jersey, are susceptible to flooding, but vary in watershed size and extent of riverine influence. Study focus This study develops a spatio-temporal neural network that accurately infers overland and riverine flow dynamics from rainfall. Instead of conventional gridded approaches, this study utilizes unstructured graph representation of hydraulic computation points to preserve physical connectivity. A novel, importance-weighted loss function increases emphasis on high-priority areas while mitigating high-value dominance from channel regions. Incorporating synthetic rainfall patterns exposes the model to irregular storm sequences, bridging historical events and normalized design storms. New hydrological insights for the region The results indicate that the FloodMAGNet effectively predicts peak flood depths aligned with risk assessment objectives. Training with synthetic rainfall sequences established strong predictive capacity for non-monotonic events, demonstrated by an R 2 of 0.975 and a 3.3 cm RMSE on high-priority nodes for an observed hyetograph in Englewood. Evaluation of design storms in Irvington yielded an RMSE of 8.3 cm across the full domain and 5.8 cm in prioritized areas. Furthermore, evaluating the larger, river-dominated watershed in Paterson resulted in a baseline RMSE of 29.3 cm, which improved to 14.4 cm after loss function recalibration, demonstrating the framework’s methodological generalizability to distinct topographies and hydrologic responses.