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.

中文翻译：

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自组织地图技术和模糊c-means聚类在韩国首尔大城市地下水质量评价中的应用

摘要 为了对首尔市地下水质量进行整体评估，我们使用自组织图（SOM）技术结合模糊 c 均值（FCM）聚类。SOM 在 2D 表面上可视化复杂的多维数据结构，而 FCM 算法在连续分布在数据空间上的样本之间创建重叠的集群边界。SOM 和 FCM 聚类的结合有望帮助表征高度复杂的城市地下水质量。因此，SOM 使用 91 个神经元对 343 个地下水样本进行了表征，这些样本通过 FCM 聚类进一步分为三个水组。第 1 组处理污染最少的地下水（17% 的样本（n = 58），平均 TDS = 194.5 mg/L 和 NO3 = 6。9 mg/L)，发生在土地覆盖以森林为主的周边地区。pH 随着钠和碳酸氢盐浓度的增加而增加，表明第 1 组的水文地球化学主要受水-岩相互作用控制。第 2 组包括高度污染的地下水（样本的 24%（n = 82），平均 TDS = 326.2 mg/L 和 NO3 = 42.6 mg/L），并且在首尔零星出现，没有明显的空间控制。这群人似乎受到了下水道破裂污水的影响，下水道是首尔地下水的主要污染源，在城市地下无处不在。第 3 组水也代表高度污染的地下水（样本的 30%（n = 103），平均 TDS = 527.1 mg/L），但含有低硝酸盐浓度（平均 NO3 = 13.1 mg/L）。根据它们的空间位置，从地铁隧道和市中心其他地下空间抽取的大量地下水似乎推动了有机污染物的诱导流动，导致局部还原条件足以进行反硝化。其余 100 个样本（占样本的 29%）具有两到三组的水文地球化学特性。本研究成功地表征了因各种污染源和水文地球化学过程而复杂化的城市地下水质量空间格局。SOM 和 FCM 聚类的结合使用被证明是一种强大的工具，可以解释难以定义聚类边界的非线性和高度异构的环境数据。总之，我们的研究结果有助于在人为污染的高风险下更好地管理大都市的城市地下水。