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

Cited 1 time in WEB OF SCIENCE Cited 1 time in Scopus
Title
Data-driven Modeling of Coastal Water Quality using the Bayesian Method for Coastal Management
Author(s)
Kim, Jinah; Choi, Jungwoon
KIOST Author(s)
Kim, Jin Ah(김진아)Choi, Jung Woon(최정운)
Publication Year
2016-03
Abstract
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 Carlo method was applied to identify an optima] data-driven model. There are no limitations of statistical assumptions for samples using the Bayesian method, which is required in a frequentist approach, such as the maximum likelihood method. The Saemangeum tidal lake's coastal water quality system was quantitatively described and assessed by interpreting coefficients of model parameters with relation among variables from a derived structural equation model. Moreover, a prediction for coastal management was possible by Bayesian inference. Thus, there are new findings on the salinity threshold necessary to maintain optimal water by improving degraded water quality. Based on the findings, a quantity of water mixing (exchaning fresh water through sluice gates) can be applied while continuing construction of land reclamation.
ISSN
0749-0208
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/2227
DOI
10.2112/SI75-130.1
Bibliographic Citation
JOURNAL OF COASTAL RESEARCH, pp.647 - 651, 2016
Publisher
COASTAL EDUCATION & RESEARCH FOUNDATION
Keywords
Data-driven modeling; Bayesian method; multivariate statistical model; coastal wetter quality; the Saemangeum coast
Type
Article
Language
English
Document Type
Article; Proceedings Paper
Publisher
COASTAL EDUCATION & RESEARCH FOUNDATION
Related Researcher
Research Interests

AI/Machine Learning,Climate Change,Marine Disaster,인공지능/기계학습,기후변화,해양기상재해

Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse