New Data-Driven Model for Shoreline Position Estimation: Addressing Cross-Shore and Alongshore Sediment Interactions

Prasath, G. and Sannasiraj, 2024. New data-driven model for shoreline position estimation: Addressing cross-shore and alongshore sediment interactions. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 272-277. Charlotte (North Carolina), ISSN 0749-0208. Coastal regions have varied land forms, reflecting the dynamic interplay between terrestrial and marine systems and anthropological influences. This critical interface, where land meets sea, is governed by a complex web of processes involving marine, terrestrial, atmospheric, biospheric, and tectonic factors. Despite significant advancements in coastal research, quantifying nearshore sediment movement remains a formidable challenge. In nearshore sediment dynamics, a comprehensive, generic description capable of quantifying the intricacies of sediment transport is difficult to formulate. Consequently, existing research often compartmentalizes the study of sediment movement into two distinct processes: cross-shore and alongshore sediment transport. The varying temporal and spatial scales of beach changes are difficult to capture in a numerically simple model. Building upon extensive shoreline observations conducted over three years along the Mahabalipuram coast, situated on the eastern seaboard of India, our research sheds new light on shoreline variations induced by the presence of shore protection structures. This paper proposes an alternative data-driven modeling framework leveraging Kalman filters to integrate processes across varying spatial and temporal scales. An alternative definition for wave forcing that uses the cross-shore wave energy flux at the breaker line and the direction fixing parameter has been narrowed down based on field observations is proposed in the model. By capitalizing on the predictable responses of nearshore morphology to changing wave conditions, our approach offers a workaround for synthesizing complex sediment dynamics into a computationally effective and easy-to-use modeling framework. Unlike traditional models, this alternative Data-driven model helps capture the nuanced interactions between sediment processes and coastal structures. Field data collected from November 2020 to 2023, empirically validate our alternative modeling formulation. Our findings demonstrate that the proposed approach outperforms traditional models in accurately predicting the shoreline changes within the study area. Moreover, by harnessing measured data to fine-tune prediction equations, the critical gaps in system knowledge can be avoided temporarily in the model formulation, and the predictive capabilities of modeling efforts can also be improved.