Clustering of Temporal Profiles Using a Bayesian Logistic Mixture Model: Analyzing Groundwater Level Data to Understand the Characteristics of Urban Groundwater Recharge
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Joo, Yongsung | - |
dc.contributor.author | Brumback, Babette | - |
dc.contributor.author | Lee, Keunbaik | - |
dc.contributor.author | Yun, Seong-Taek | - |
dc.contributor.author | Kim, Kyoung-Ho | - |
dc.contributor.author | Joo, Chaeman | - |
dc.date.accessioned | 2021-09-08T14:00:41Z | - |
dc.date.available | 2021-09-08T14:00:41Z | - |
dc.date.created | 2021-06-10 | - |
dc.date.issued | 2009-09 | - |
dc.identifier.issn | 1085-7117 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/119417 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | SPRINGER | - |
dc.subject | AQUIFER | - |
dc.subject | SEOUL | - |
dc.title | Clustering of Temporal Profiles Using a Bayesian Logistic Mixture Model: Analyzing Groundwater Level Data to Understand the Characteristics of Urban Groundwater Recharge | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Yun, Seong-Taek | - |
dc.identifier.doi | 10.1198/jabes.2009.07100 | - |
dc.identifier.scopusid | 2-s2.0-77951699961 | - |
dc.identifier.wosid | 000270370000006 | - |
dc.identifier.bibliographicCitation | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, v.14, no.3, pp.356 - 373 | - |
dc.relation.isPartOf | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS | - |
dc.citation.title | JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS | - |
dc.citation.volume | 14 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 356 | - |
dc.citation.endPage | 373 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Life Sciences & Biomedicine - Other Topics | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Biology | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | AQUIFER | - |
dc.subject.keywordPlus | SEOUL | - |
dc.subject.keywordAuthor | Clustering of time course data | - |
dc.subject.keywordAuthor | Hydrogeology | - |
dc.subject.keywordAuthor | Model-based clustering | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.