Development of an Early Warning System for Compound Coastal and Fluvial Flooding: Implementation at the Alfeios River Mouth, Greece

An integrated early warning system (EWS) for compound coastal and fluvial flooding is developed for Pyrgos, Western Greece, where low-lying geomorphology and past storm events highlight the need for rapid, impact-based forecasting. The methodology couples historical and climate-informed metocean and river discharge datasets within a numerical modeling framework consisting of a mild-slope wave model, the CSHORE coastal profile model, and HEC-RAS 2D inundation simulations. A weighted K-Means clustering approach is used to generate representative extreme scenarios, yielding more than 4000 coupled simulations that train and validate Artificial Neural Networks (ANNs). The optimal feed-forward ANN accurately predicts spatially distributed flood depths across the HEC-RAS grid using only offshore wave characteristics, water level, and river discharge as inputs, reducing computation time from hours to seconds. Blind tests demonstrate close agreement with full numerical simulations, with average differences typically below 5% and minor deviations confined to negligible water depths. These results confirm the ANN’s capability to emulate complex compound flooding dynamics with high computational efficiency. Deployed as a web application (EWS_CoCoFlood), the system provides actionable, near-real-time inundation forecasts to support local civil protection authorities. The framework is modular and scalable, enabling future integration of urban and rainfall-induced flooding processes and coastal morphological change.