TY - JOUR
T1 - Clustering of temporal profiles using a Bayesian logistic mixture model
T2 - Analyzing groundwater level data to understand the characteristics of urban groundwater recharge
AU - Joo, Yongsung
AU - Brumback, Babette
AU - Lee, Keunbaik
AU - Yun, Seong Taek
AU - Kim, Kyoung Ho
AU - Joo, Chaeman
PY - 2009
Y1 - 2009
N2 - The hydrogeologic conditions of groundwater can be examined by carefully studying the patterns of fluctuations in groundwater levels. These fluctuations are spatially and temporally influenced by many complicated factors, including rainfall, topography, land use, and hydraulic properties of soils and bedrock (i.e., aquifers). In this article we report a methodology based on the Bayesian logistic mixture model to simultaneously cluster profiles of groundwater level changes over time and estimate the relationships between the characteristics of each cluster and environmental variables. We apply the proposed method to analyze groundwater level profiles from 37 monitoring wells in Seoul, South Korea, and we find four clusters of wells. Using the estimated relationship between the clusters and the environmental variables, we discern the hydrogeologic conditions of each cluster, thus gaining insight into the recharge and subsurface flow of bedrock groundwater in an urban setting and the vulnerability of groundwater to the inflow of potential pollutants from ground surface. This article has supplementary material online.
AB - The hydrogeologic conditions of groundwater can be examined by carefully studying the patterns of fluctuations in groundwater levels. These fluctuations are spatially and temporally influenced by many complicated factors, including rainfall, topography, land use, and hydraulic properties of soils and bedrock (i.e., aquifers). In this article we report a methodology based on the Bayesian logistic mixture model to simultaneously cluster profiles of groundwater level changes over time and estimate the relationships between the characteristics of each cluster and environmental variables. We apply the proposed method to analyze groundwater level profiles from 37 monitoring wells in Seoul, South Korea, and we find four clusters of wells. Using the estimated relationship between the clusters and the environmental variables, we discern the hydrogeologic conditions of each cluster, thus gaining insight into the recharge and subsurface flow of bedrock groundwater in an urban setting and the vulnerability of groundwater to the inflow of potential pollutants from ground surface. This article has supplementary material online.
KW - Clustering of time course data
KW - Hydrogeology
KW - Model-based clustering
UR - http://www.scopus.com/inward/record.url?scp=77951699961&partnerID=8YFLogxK
U2 - 10.1198/jabes.2009.07100
DO - 10.1198/jabes.2009.07100
M3 - Article
AN - SCOPUS:77951699961
SN - 1085-7117
VL - 14
SP - 356
EP - 373
JO - Journal of Agricultural, Biological, and Environmental Statistics
JF - Journal of Agricultural, Biological, and Environmental Statistics
IS - 3
ER -