Leveraging Coupled Hydrodynamic with Data-Driven GeoAI Models for Advancing Systemic Compound Flood Risk Evaluation in Coastal Urban Areas

Compound flood (CF) represents a complex hazard that often leads to severe impacts. CF propagates across interconnected systems, generating systemic societal and environmental risks, particularly in coastal cities. Despite progress in data science and remote sensing, a comprehensive review of coupled hydrodynamics with the data-driven GeoAI—an integration of geospatial analysis and artificial intelligence (AI)—for systemic CF risk remains scarce. This review summarizes foundational data-driven and numerical approaches in CF modeling. It then synthesizes emergence, utilization modes, and advancements of coupled hydrodynamic-GeoAI frameworks for CF prediction and systemic impact quantification. A systematic review follows the PRISMA protocol, examining 403 articles from the Web of Science and Scopus databases. The concept of the coupled hydrodynamics-GeoAI model synergizes physics-based simulations with data-driven computational learning, enhancing predictive accuracy and spatially detailed flood risk while explicitly embedding geographic features into the framework. The model offers three utilization modes: (i) direct coupling, (ii) surrogate modeling, and (iii) stochastic statistical-hydrodynamic-ML framework. To enhance comprehensive and robust risk assessment, the review proposes four key model advancements: (1) implementing an active learning framework, (2) integration with physics-guided data-driven, (3) dynamically coupling CF drivers with external factors, and (4) incorporating spatiotemporal analysis under changing climate and socioeconomic conditions. We further advocate for integrating the quantification of both tangible and intangible cascading impacts into systemic CF risk assessments. This review synthesizes computational strategies integrating physics-based hydrodynamics with GeoAI, providing a foundation for systemic CF risk evaluation and guiding future advances in computational hydrology and resilient urban flood management.

© The Author(s) 2025