Employing cluster analysis in defining groundwater wells patterns in Rafah

Clustering analysis techniques used in identifying homogeneity of groundwater wells patterns in terms of Chloride, Nitrate, and TDS. Data was collected through the laboratories of Palestinian Water Authority in the Rafah 2020. R-Programming is used for data analysis. Kolmogorov-Smirnov test is used to test data normality. Hierarchical clustering analysis applied to generate a cluster tree (r>0.75) in correlation matrix. Agglomerative Hierarchical Clustering is used to classify with “average” method and found that there are two clusters. First cluster with 28 wells and Second cluster with 9 wells. Test of homogeneity between the two clusters groups using T-test for the hierarchical clustering and found that there are significant differences in means for TDS and for Chloride between first cluster and second cluster with a significant level less than 0.01. While for Nitrates, also, there are significant differences in means between the first cluster and second cluster with a significant level less than 0.05. Different stability validation measures carried out for data concludes that for the APN and ADM measures, hierarchical clustering with two clusters again gives the best score. Three internal cluster validation measures are used. Concludes that Hierarchical clustering with two clusters performs the best in (Connective, Dunn, and Silhouette). Recall that the connectivity should be minimized, while both the Dunn Index and the Silhouette Width should be maximized. Thus, Hierarchical clustering outperforms the other clustering algorithms under each validation measure.