MACHINE LEARNING CLASSIFICATION OF BEACH STATE FROM ARGUS IMAGERY
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How to Cite

Ellenson, A., Simmons, J., Wilson, G., Hesser, T., & Splinter, K. D. (2020). MACHINE LEARNING CLASSIFICATION OF BEACH STATE FROM ARGUS IMAGERY. Coastal Engineering Proceedings, (36v), sediment.37. https://doi.org/10.9753/icce.v36v.sediment.37

Abstract

Nearshore beach morphology is of interest to coastal managers due to the strong influence it exerts on subaerial beach erosion, pollutant dispersal, and recreational safety. In particular, wave breaking conditions and nearshore hydrodynamics are highly dependent on sandbar configuration. The term 'beach state' describes specific planform configurations of nearshore morphology that are in dynamic equilibrium with the time-varying forcing conditions. Beach state categories were first introduced by Wright and Short (1984), who observed sandbar systems in Narrabeen-Collaroy, Australia and extended by Lippman and Holman (1990), based on observations of time-exposure Argus imagery of sandbar systems in at Duck, NC, USA. In this study, we use machine learning algorithms to identify beach states from Argus imagery at two distinct sites: Narrabeen-Collaroy (hereafter Narrabeen), SE Australia, and Duck, NC. We assess the ability of the algorithm to classify beach states at each site and its transferability from one beach to another. Additionally, we investigate the extent to which the spatial and temporal evolution of beach states influences the ability of the algorithm to classify images into discrete beach states.

Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/38OM8CseIww
https://doi.org/10.9753/icce.v36v.sediment.37
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References

Lippmann, T. C., and R. A. Holman. "The spatial and temporal variability of sand bar morphology." Journal of Geophysical Research: Oceans 95, no. C7 (1990): 11575-11590.

Wright, Lynn D., and Andrew D. Short. "Morphodynamic variability of surf zones and beaches: a synthesis." Marine geology 56, no. 1-4 (1984): 93-118.

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