Shoreline Change Prediction: A Systematic Review of Multidisciplinary Approaches for Sustainable Coastal Management and Climate Resilience

Coastal shorelines are among the most dynamic and socioeconomically valuable environments on Earth. However, predictions of shoreline change remain constrained by a lack of interdisciplinary integration, poor spatiotemporal validation, insufficient quantification of uncertainties, and spatial data biases. This systematic review, conducted in accordance with PRISMA 2020 guidelines, synthesizes 581 peer-reviewed studies on shoreline prediction indexed in Scopus between 2019 and 2025. The review framework spans influencing factors, monitoring and data acquisition, shoreline extraction methods, quantification frameworks, predictive modeling approaches, and operational applications. Medium-resolution optical satellite platforms dominate observational practice, accounting for 61% of all sensing platforms used in the literature, whereas synthetic aperture radar (7.9%) and unmanned aerial systems (20.6%) remain underutilized, despite their complementary capabilities in dynamic coastal environments. These findings indicate that statistical quantification frameworks remain structurally dependent on linear assumptions, which are fundamentally incompatible with the threshold-driven behavior of real coastal systems. Process-based numerical models are led by XBeach, which accounts for 22.5% of models and demonstrates strong calibration performance but limited forward predictive transferability across sites and forcing conditions. Artificial intelligence applications expanded by 162.5% over the study period, converging toward physics-informed neural networks that directly embed physical conservation laws into model training and achieve the optimal balance between predictive accuracy, interpretability, and computational efficiency. Geographic analysis revealed a pronounced research focus on Asian coastal environments, whereas African and South American coastlines received minimal attention, despite their disproportionate vulnerability to erosion and population exposure. These findings highlight research priorities oriented toward standardized benchmarking, probabilistic forecasting, and transferable hybrid modeling architectures aligned with the United Nations Sustainable Development Goals for climate action, ocean conservation, and sustainable coastal communities.