A Generalised 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 generalisation across diverse coastal environments. In this study, we introduce a generalised, 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 generalises to 30 unseen sites (~100 km) across a five-year spatiotemporal holdout period (2020-2025), achieving a median RMSE of 12.92 m, producing forecasts without site-specific calibration. The model has difficulty in regions with complex coastal morphodynamics, like 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 contextualises the primary forcing relationship between waves and shoreline response is essential for generalisation. Our findings demonstrate that end-to-end deep learning offers a viable path toward capturing large-scale shoreline dynamics and provides early insight into what may be required to realise unified modelling frameworks capable of operating at regional or even global scales.