Comparison of river water quality assessment methods using the tree model and the nearest neighbor method (A case study: AhvazHydrometric Station)

Abstract

Ahwaz Hydrometric Station is responsible for controlling surface water resources and the Karoon River near Ahwaz city in southwestern Iran. And the present study aimed to determine the parameters affecting water quality, especially TH and SAR parameters. For this purpose, 39-year old statistical data were collected with 463 samples. To determine the water quality, first the correlation matrix method and statistical analysis were conducted, and then the correlation between them and the accuracy of these methods were checked using the tree model and the K-Nearest Neighbor (K-NN) method. The K-NN method and multivariate regression were compared for water quality characteristics, including SAR. The results indicated that K-NN methods were better than the regression method. In addition, the K-NN method using the effective anion and cation combinations yielded better results of estimating Sodium Absorption Ratio (SAR) and Total hardness (TH). Furthermore, the accuracy of the tree model after estimating TH using SO42- was more than that of Ca2+. Moreover, the accuracy of the tree model using the Cl- data for SAR estimation was higher than that of the Na+ data. In general, according to the APHA standard (2005), river water is in the high-risk and low-alkaline group.

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