TY - JOUR
T1 - The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea
AU - Lee, Kyung Jin
AU - Yun, Seong Taek
AU - Yu, Soonyoung
AU - Kim, Kyoung Ho
AU - Lee, Ju Hee
AU - Lee, Seung Hak
N1 - Funding Information:
This work was supported by the National Research Foundation of Korea Grant funded by the South Korean Government (University-Institute cooperation program) and partially by the Korea CO 2 Storage Environmental Management (K-COSEM) Research Center funded by Korea Environmental Industry & Technology Institute . The initial sampling and analysis of groundwater for this study was supported by the Seoul Institute . Constructive comments and suggestions by editors and reviewers were helpful to clarify and improve the manuscript.
Publisher Copyright:
© 2018 Elsevier B.V.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/2
Y1 - 2019/2
N2 - To make an overall assessment of the groundwater quality in Seoul city, we used the self-organizing map (SOM) technique in combination with fuzzy c-means (FCM) clustering. SOM visualizes complicate and multidimensional data structures on a 2D surface while the FCM algorithm creates overlapping cluster boundaries among samples that are continuously distributed over a data space. The combination of SOM and FCM clustering was expected to help characterize highly complicated urban groundwater quality. As a result, the SOM characterized 343 groundwater samples using 91 neurons, which were further classified by FCM clustering into three water groups. Group 1 addressed the least polluted groundwater (17% of the samples (n = 58), average TDS = 194.5 mg/L and NO 3 = 6.9 mg/L) and occurred in the peripheral areas whose land cover is mainly occupied by forests. Increasing pH with increasing sodium and bicarbonate concentrations indicated that the hydrogeochemistry of Group 1 was largely controlled by water-rock interactions. Group 2 included the highly polluted groundwater (24% of the samples (n = 82), average TDS = 326.2 mg/L and NO 3 = 42.6 mg/L), and sporadically occurred in Seoul, with no distinct spatial control. This group seemed to be affected by sewage from broken sewer pipes, which are a primary pollution source of Seoul groundwater and are ubiquitously distributed beneath the city. Group 3 water also represented the highly contaminated groundwater (30% of the samples (n = 103), average TDS = 527.1 mg/L), but contained low nitrate concentrations (average NO 3 = 13.1 mg/L). Based on their spatial locations, intensive groundwater pumping from subway tunnels and other underground spaces at the city center seemed to drive the induced flow of organic contaminants, resulting in local reducing conditions sufficient for denitrification. The remaining 100 samples (29% of the samples) shared the hydrogeochemical properties of two or three groups. This study successfully characterized the spatial pattern of urban groundwater quality that is complicated by various contamination sources and hydrogeochemical processes. The combined use of SOM and FCM clustering was proven as a powerful tool to interpret nonlinear and highly heterogeneous environmental data for which it is difficult to define cluster boundaries. Taken together, our results contribute to a better management of urban groundwater in metropolitan cities under high risks of anthropogenic contamination.
AB - To make an overall assessment of the groundwater quality in Seoul city, we used the self-organizing map (SOM) technique in combination with fuzzy c-means (FCM) clustering. SOM visualizes complicate and multidimensional data structures on a 2D surface while the FCM algorithm creates overlapping cluster boundaries among samples that are continuously distributed over a data space. The combination of SOM and FCM clustering was expected to help characterize highly complicated urban groundwater quality. As a result, the SOM characterized 343 groundwater samples using 91 neurons, which were further classified by FCM clustering into three water groups. Group 1 addressed the least polluted groundwater (17% of the samples (n = 58), average TDS = 194.5 mg/L and NO 3 = 6.9 mg/L) and occurred in the peripheral areas whose land cover is mainly occupied by forests. Increasing pH with increasing sodium and bicarbonate concentrations indicated that the hydrogeochemistry of Group 1 was largely controlled by water-rock interactions. Group 2 included the highly polluted groundwater (24% of the samples (n = 82), average TDS = 326.2 mg/L and NO 3 = 42.6 mg/L), and sporadically occurred in Seoul, with no distinct spatial control. This group seemed to be affected by sewage from broken sewer pipes, which are a primary pollution source of Seoul groundwater and are ubiquitously distributed beneath the city. Group 3 water also represented the highly contaminated groundwater (30% of the samples (n = 103), average TDS = 527.1 mg/L), but contained low nitrate concentrations (average NO 3 = 13.1 mg/L). Based on their spatial locations, intensive groundwater pumping from subway tunnels and other underground spaces at the city center seemed to drive the induced flow of organic contaminants, resulting in local reducing conditions sufficient for denitrification. The remaining 100 samples (29% of the samples) shared the hydrogeochemical properties of two or three groups. This study successfully characterized the spatial pattern of urban groundwater quality that is complicated by various contamination sources and hydrogeochemical processes. The combined use of SOM and FCM clustering was proven as a powerful tool to interpret nonlinear and highly heterogeneous environmental data for which it is difficult to define cluster boundaries. Taken together, our results contribute to a better management of urban groundwater in metropolitan cities under high risks of anthropogenic contamination.
KW - Fuzzy c-means (FCM) clustering
KW - Hydrogeochemistry and quality
KW - Self-organizing map (SOM)
KW - Urban groundwater
KW - Urban water management
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U2 - 10.1016/j.jhydrol.2018.12.031
DO - 10.1016/j.jhydrol.2018.12.031
M3 - Article
AN - SCOPUS:85059483138
SN - 0022-1694
VL - 569
SP - 685
EP - 697
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -