Comparative assessment of AI-based and classical DSAS approaches in multi-temporal shoreline prediction: A case study of Ras El-Bar coast, Egypt

Along Ras El-Bar coast, NE Nile Delta of Egypt, intense human interventions and natural processes give rise to highly distinctive non-linear shoreline dynamics that challenge the stationarity assumptions of traditional forecasting. Classical methods, specifically the Digital Shoreline Analysis System Linear Regression Rate (DSAS-LRR), often fail to capture abrupt anthropogenic regime shifts induced by engineering structures. This study presents a comparative assessment of DSAS-LRR against two artificial intelligence (AI) recurrent neural networks, Long Short-Term Memory (LSTM) and Nonlinear Autoregressive Exogenous (NARX), using a multi-decadal satellite-derived shoreline record (1982–2024) to project shoreline evolution through 2050. Results show DSAS-LRR unrealistic projections, exceeding 250 m of displacement by 2050, due to its inability to account for rapid anthropogenic interventions. Conversely, AI models successfully captured complex temporal responses. While LSTM provided conservative estimates, the intervention-aware NARX model achieved the highest predictive accuracy and spatial consistency. Model performance was rigorously evaluated via Taylor diagrams, Performance Index Metric (PIm), and transect-based RMSE analysis, and was further validated by an independent 2025 “blind test”. NARX consistently outperformed both models, accurately reproducing accretion in breakwater shadow zones and moderate erosion between structures, with average RMSE values of 6–14 m. These findings underscore that for anthropogenically modified coasts, intervention-aware AI is no longer just an alternative, it is an essential tool for reliable prediction. The proposed framework provides a transferable roadmap for evidence-based coastal management and infrastructure planning in vulnerable deltaic systems worldwide.