A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Abstract Simulating compound flooding (CF) at the river‐ocean interface within large‐scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics‐informed and data‐driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data‐driven neural network models (i.e., CNNs, U‐Net, Long Short‐Term Memory (LSTM), Gated Recurrent Unit), and physics‐informed neural network (PINN) models, including vanilla PINN and a finite‐difference‐based PINN (FD‐PINN). Specifically, FD‐PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data‐driven model training, a new data‐generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one‐dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD‐PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data‐driven models, the best overall performance is achieved by a CNN‐LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN‐FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence‐aware designs for robust generalization. This study reveals the trade‐offs between physics‐informed and data‐driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.