The study of coastal morpho-dynamics refers to the complex interactions between environmental forcing parameters like winds, waves, tides, and currents and the resultant changes to the exposed beaches over a wide range of time and space scales.

Coastal management, design of coastal structures, and flood risk all depend upon our understanding of how the shoreline changes, especially at decadal and longer timescales. Understanding the physical processes that govern mesoscale morphological evolution is crucial for successfully designing coastal defense systems and formulating shoreline management strategies.

Data-driven models attempt to establish a suitable relationship between the wave forcing derived from offshore wave parameters observed at close time intervals and the sparsely measured shoreline positions ( from field studies/observations), enabling shoreline evolution simulation. Such models are, by design, numerically fast and use heuristic or observation‐based rules. They have the potential to couple processes of different space-time scales in morphological evolution based chiefly on the anticipated coherent and predictable responses of the nearshore morphology to the changing incident wave conditions.

The present research uses data from a detailed field measurement study at the Mahabalipuram coast for one year ( Nov 2020 to 2021 ) to develop an improved data-driven model formulation that has the potential to improve shoreline evolution modeling.