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Clustering of Temporal Profiles Using a Bayesian Logistic Mixture Model: Analyzing Groundwater Level Data to Understand the Characteristics of Urban Groundwater Recharge

Authors
Joo, YongsungBrumback, BabetteLee, KeunbaikYun, Seong-TaekKim, Kyoung-HoJoo, Chaeman
Issue Date
9월-2009
Publisher
SPRINGER
Keywords
Clustering of time course data; Hydrogeology; Model-based clustering
Citation
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, v.14, no.3, pp.356 - 373
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
Volume
14
Number
3
Start Page
356
End Page
373
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/119417
DOI
10.1198/jabes.2009.07100
ISSN
1085-7117
Abstract
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.
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