Estimates of Sediment Pickup Rate induced by Surge Wave within a Multilevel Bayesian Regression Framework

Abstract

Jeong, S.I.; Kwon, H.-H., and Lee, S.O., 2018. Estimates of Sediment Pickup Rate Induced by Surge Wave Within a Multi-level Bayesian Regression Framework. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 286–290. Coconut Creek (Florida), ISSN 0749-0208.A coastal erosion was caused by the tractive force of wave, and is accelerating in some areas. In this study, a hydraulic experiment considered the pickup concept was carried out to understand this phenomenon. Since existing methods for deriving suspended sediment concentrations (SSC) were likely to affect the results, image processing techniques was used. Preliminary experiments were conducted for this process. Because relevant data necessarily involves uncertainty, a two-step Bayesian Markov Chain Monte Carlo method (MCMC) was used to quantitatively derive it. The first step was about the relationship between image grayscale and turbidity, and then relationship between SSC and turbidity was presented for quantative analysis to show sediment pickup rate. A sluice gate was designed for rapid openning to generate a solitary wave, and the optimum opening speed was derived. The experimental result indicated that the physical characteristics of wave and suspended sediment pickup rate were closely related, and this relationship was changed according to wave breaking. And solitary wave pickup function was presented by adapting Einstein (1950)'s essential concepts. More precise pickup rate can be used as basic data for prevention of coast erosion and management of shoreline.