Ground water quality assessment using pattern recognition techniques: A case study on Khafr aquifer (Jahrom, Iran)
الموضوعات : Assessment and Management of Water, Soil, Air and Noise Pollution and Ecosystems Health
1 - مرودشت
الکلمات المفتاحية: Groundwater quality, Cluster Analysis, Pattern Recognition Technique, Principal Component Analysis. ,
ملخص المقالة :
The knowledge of physicochemical characteristics has special significance in arid and semi-arid zones where ground water (GW) is the main source of water. The study of GW hydrochemistry of the Khafr aquifer in Jahrom (Fars, Iran) produced a large geochemical dataset. GW samples were collected at 18 sites, and analyzed for 12 variables. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to the dataset to evaluate their usefulness to classify GW samples, and identify geochemical processes. Scree plot extracted two significant components explaining 93% of total variance. Component 1 with high and positive scores on Ca2+, K+, Cl-, Na+, TDS, EC, SO42-, and SAR, explains 55.5% of variance, pointing to a common origin for these minerals like dissolution of limestone, marl and gypsum in water. Component 2 containing 37.5% of variance, includes high and positive scores on TH, HCO3-, SO42-, Ca2+ and Mg2+, and negative on pH. From their loadings, the components were defined as the salinity and hardness. PCA in combination with CA has nearly allowed identification and assessment of spatial sources of variation affecting hydrochemistry of GW. CA was not able to define details but guided through classification. Well 3 in the neighbourhood of salty braid of Ghareh Aghaj river was recognized as the most polluted one. The results will be helpful for the decision makers to adopt suitable remedial measures to protect GW sources.
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