Understanding future shoreline evolution is fundamental to developing adaptive coastal management strategies under climate change scenarios. This study analyzes the Southern Baltic Sea’s ∼37-km stretch comprising Usedom and Wolin islands, where sandy coastlines face intensifying erosion threats under rising anthropogenic and climatic pressures. We introduce an explainable Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) framework designed to bridge the gap between Deep Learning (DL) performance and physical interpretability in decadal forecasting. The results show that the best model achieves a Root Mean Squared Error (RMSE) of 10.40 m, Mean Absolute Error (MAE) of 7.13 m, and R-squared (R 2 ) of 0.55. Deep SHapley Additive exPlanation (DeepSHAP) attribution reveals that erosion is driven by the compound interaction of sea-level rise (SLR), storm surges, and extreme waves. This transferable framework represents a significant methodological contribution, enhancing regional early-warning systems and providing a robust, "white-box" approach for Baltic Sea’s operational coastal management. • A sequence-aware neural network model forecasts decadal shoreline change until 2050. • The best model achieves an R-squared value of 0.55 for decadal shoreline change. • Explainable models show erosion is driven by sea levels and extreme waves. • Widespread erosion intensification is projected for the Southern Baltic by 2050.
Tanwari et al. (Sun,) studied this question.