Total suspended matter (TSM) in coastal areas is affected by various sediment transport mechanisms. Thus, identifying systematically and robustly the regional pattern of TSM data is of great use for the management of coastal areas. The TSM sampling stations have been regionally clustered using an iterative toroidal SOM–k–means approach, which overcomes the cluster number determination and local optima problems. The iterative toroidal SOM–k–means is a two-step clustering approach that finds an optimal clustering result based on DBI by repeating the toroidal SOM and k-means clustering. Six variables (longitude, latitude, surface TSM in February and August, bottom TSM in February and August) were used as clustering variables, and cluster analyses were conducted while varying combinations of variables. All clustering results were analyzed using the sediment distribution, ocean currents, and tidal currents around South Korean coastal areas. Additionally, tidal flats around the Yeomha Channel, Hampyeong Bay, and Doam Bay, which are associated with sediment dispersion of turbid water, bay erosion, and tidal forcing, respectively, were identified during clustering analyses. This study's outcome provides the basis of regional sediment transport analysis in South Korea and an example of physical analysis based on the data-driven approach.

中文翻译：

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韩国沿海地区总悬浮物的区域分类

沿海地区的总悬浮物（TSM）受到各种沉积物输送机制的影响。因此，系统地、稳健地识别 TSM 数据的区域模式对于沿海地区的管理具有很大的用途。TSM 采样站使用迭代环形 SOM-k-means 方法进行区域聚类，克服了聚类数量确定和局部最优问题。迭代环形 SOM-k-means 是一种两步聚类方法，通过重复环形 SOM 和 k-means 聚类来找到基于 DBI 的最佳聚类结果。以经度、纬度、2月和8月地面TSM、2月和8月底部TSM 6个变量作为聚类变量，在不同变量组合的情况下进行聚类分析。所有聚类结果均使用韩国沿海地区周围的沉积物分布、洋流和潮汐流进行分析。此外，在聚类分析中还发现了盐河海峡、咸平湾和洞岩湾周围的潮滩，它们分别与浑水的沉积物扩散、海湾侵蚀和潮汐强迫有关。这项研究的结果为韩国区域沉积物输送分析提供了基础，并提供了基于数据驱动方法的物理分析示例。