A Generalized Data‐Driven Shoreline Model at the Regional Scale

Shoreline models underpin long‐term coastal planning and hazard mitigation but are often limited by site‐specific calibration and simplified process representations. While machine learning (ML) approaches have recently matched or exceeded the skill of traditional models in deterministic shoreline forecasting, they have yet to demonstrate generalization across diverse coastal environments. In this study, we introduce a generalized, data‐driven regional‐scale shoreline model trained across 2,000 km of the southeast Australian coastline. The model adapts the Temporal Fusion Transformer (TFT) architecture, combining a sequence‐to‐sequence Long Short‐Term Memory (LSTM) module with masked multi‐head self‐attention to learn both short‐ and long‐range temporal dependencies. Static covariates unique to each transect are integrated via gating mechanisms that condition the learned relationship between wave action and shoreline response, enabling outputs to adapt to local site characteristics. The model generalizes to 30 unseen sites (∼100 km) across a 5‐year spatiotemporal holdout period (2020–2025), achieving a median Root Mean Squared Error of 12.92 m, producing forecasts without site‐specific calibration. The model has difficulty in regions with complex coastal morphodynamics, such as river entrances and coastal structures that introduce more nuanced relationships that are not explicitly accounted for. Feature analysis further reveals that site‐specific information that contextualizes the primary forcing relationship between waves and shoreline response is essential for generalization. Our findings demonstrate that end‐to‐end deep learning provides a viable pathway for developing regionally trained, generalized shoreline models, and motivates further exploration into the possibility of multi‐regional models.