The combined use of self-organizing map technique and fuzzy c-means clustering to evaluate urban groundwater quality in Seoul metropolitan city, South Korea

Abstract

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 NO3 = 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 NO3 = 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 NO3 = 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.