Abstract

This study proposes a new approach to establish a multi-parameter risk mapping method by employing the K-Means clustering technique. Accordingly, spatial assessment of arsenic (As), nitrate (NO₃) and total dissolved solids (TDS) were carried out based on the type of land use to estimate contamination potential in an aquifer. Since risk mapping is always associated with the occurrence probability of a phenomenon, pollution occurrence probability was then obtained using the fuzzy C-means clustering. The results reveal that NO₃ and As contamination levels increase from the first cluster (C1), covers 22.3% of the aquifer, to C5 encompassing 35.1% of the aquifer devoted to extensive industrial and agricultural activities. Fuzzy clustering results show that the pollution occurrence probability in each aquifer cell varied from less than 30 to more than 90%. Moreover, the results show, industrial and agricultural land uses cover about 70% of the areas with high risk of contamination.

摘要

本研究提出了一种利用 K-Means 聚类技术建立多参数风险映射方法的新方法。因此，根据土地利用类型对砷 (As)、硝酸盐 (NO ₃ ) 和总溶解固体 (TDS) 进行了空间评估，以估计含水层中的污染潜力。由于风险映射总是与现象的发生概率相关联，因此使用模糊 C 均值聚类获得污染发生概率。结果表明 NO ₃随着污染水平从第一个集群 (C1) 增加，涵盖 22.3% 的含水层，到 C5，涵盖 35.1% 的用于广泛的工业和农业活动的含水层。模糊聚类结果表明，每个含水层单元的污染发生概率从小于30%到大于90%不等。此外，研究结果表明，工农业用地覆盖了约70%的污染高风险地区。