Data-driven Modeling of Coastal Water Quality using the Bayesian Method for Coastal Management

Abstract

ABSTRACT Kim, J. and Choi, J, 2016. Data-driven modeling of coastal water quality using the Bayesian method for coastal management. In: Vila-Concejo, A.; Bruce, E.; Kennedy, D.M., and McCarroll, R.J. (eds.), Proceedings of the 14th International Coastal Symposium (Sydney, Australia). Journal of Coastal Research, Special Issue, No. 75, pp. 647–651. Coconut Creek (Florida), ISSN 0749-0208. To understand and a predict a coastal water quality system, a data-driven statistical model has been proposed using the Bayesian method and applied to the Saemangeum tidal lake. To describe a coastal water quality system, a multivariate statistical model was derived by determining observed variables and their interrelationships such as sea surface temperature, salinity, Chl-a, DO, pH, TN, TP, COD, NH4N, NO2N, NO3N, PO4O, and SiO2Si for parameters of coastal marine environments, coastal water quality, and nutrients using observed field data. To estimate this statistical model, a Bayesian approach using Markov chain Monte C...