2012
DOI: 10.1002/hyp.9245
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Multivariate statistical analysis and environmental isotopes of Amman/Wadi Sir (B2/A7) groundwater, Yarmouk River Basin, Jordan

Abstract: Multivariate statistical techniques, cluster and factor analyses were applied on the Amman/Wadi Sir groundwater chemistry, Yarmouk River basin, north Jordan. The main objective was to investigate the main processes affecting the groundwater chemical quality and its evolution. The k-means cluster analysis yields three groups with distinct ionic concentrations. Cluster 1 comprises the vast majority of the sampled wells, and the water that belongs to this cluster can be classified as freshwater. Cluster 2 compris… Show more

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Cited by 20 publications
(6 citation statements)
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“…The range of the topography values observed is comparable to the distribution of the most notable major topographic features in Jordan: Wadi Mujib located at the 31,4°N 35,5°E, a river canyon entering the Dead Sea from the east, Wadi Araba (Jarar et al, 1983;Atallah, 1992;Klinger et al, 1997), Wadi Shueib catchment area (Obeidat et al, 2021), a basin of the Yarmouk River, which is the largest tributary of the Jordan River on its south (Obeidat et al, 2013), Wadi Mujib (Abed, 2017), the depression of the Araba valley, situated between the Dead Sea Fault and the Gulf of Aqaba covered by alluvial and lacustrine deposits (Klinger et al, 2000a).…”
Section: Rezultati / Resultssupporting
confidence: 64%
“…The range of the topography values observed is comparable to the distribution of the most notable major topographic features in Jordan: Wadi Mujib located at the 31,4°N 35,5°E, a river canyon entering the Dead Sea from the east, Wadi Araba (Jarar et al, 1983;Atallah, 1992;Klinger et al, 1997), Wadi Shueib catchment area (Obeidat et al, 2021), a basin of the Yarmouk River, which is the largest tributary of the Jordan River on its south (Obeidat et al, 2013), Wadi Mujib (Abed, 2017), the depression of the Araba valley, situated between the Dead Sea Fault and the Gulf of Aqaba covered by alluvial and lacustrine deposits (Klinger et al, 2000a).…”
Section: Rezultati / Resultssupporting
confidence: 64%
“…Before analysis, the data were subjected to standardization so that in parameters with low variability, they would not be masked by parameters having higher variability. This eliminated the influence of different units of measurement, and rendered the data dimensionless (Obeidat et al, 2013). The correlated variables were linearly related to form one factor represented as a gradient (trajectory or eigenvector) in multidimensional space.…”
Section: Factor Analysismentioning
confidence: 99%
“…Clustering analysis is an unsupervised pattern recognition technology that can reveal the intrinsic structure or potential features of data sets without any prior assumption. It classifies the objects of the system into categories or clusters based on their proximity or similarity, and is commonly used in the management of hydrogeochemical data [31][32][33]. Q-mode hierarchical cluster analysis (HCA) is the most widely used method.…”
Section: Cluster Analysis (Ca)mentioning
confidence: 99%
“…The loading matrix of the original variables and the common factors are obtained by the method of varimax rotation, in which each factor is described only in terms of those variables and affords greater ease for interpretation [45]. The largest loading, either positive or negative, suggests the meaning of the dimension; positive loading indicates that the contribution of variables towards the geochemical processes increases with the increasing loadings in a dimension; whereas negative loading indicates a decrease [33,46,47]. Cluster analysis and factor analysis are performed with the IBM SPSS 22 statistical software.…”
Section: Factor Analysis (Fa)mentioning
confidence: 99%