Shoreline Prediction Models: A Review of the Evolution from Empirical to AI Machine Learning Approaches

The dynamic nature of coastal zones is characterized by continuous change in shoreline position due to natural and anthropogenic processes. These changes present challenges for coastal management and conservation efforts. Traditionally, shoreline change analysis relied mainly on empirical observations and numerical models which was limited in dealing with complex, multi-dimensional interactions along our coasts. Recent decades have witnessed an integration of machine learning (ML) techniques into coastal studies to predict shoreline changes. This review aims to provide a general overview of the development of shoreline modeling and the evolution of ML applications in the field. The review synthesizes findings from 18 research papers, tracing the development of shoreline prediction methodologies from early empirical models to modern ML-based frameworks. The analysis highlights a shift from deterministic approaches to data-driven models that leverage multiple ML techniques for improved predictions. By comparing different modeling approaches over time, this study evaluates the effectiveness of ML in capturing shoreline dynamics and enhancing predictive capabilities. The review shows that new methods can significantly enhance shoreline modeling, offering improved predictive power and new insights into coastal dynamics. The findings suggest future research directions in the context of climate change and increasing human interventions.