Surface water quality prediction using data mining method (Case study: Rivers of northern side of Sahand Mountain)

Document Type : Research Article


1 Assistant Professor, Department of Water Engineering, University of Tabriz, Tabriz, Iran

2 Assistant Professor, Department of Water Engineering, Shahrekord University, Shahrekord, Iran

3 MSc MSc Student of Civil Engineering, Islamic Azad University of Maragheh, Maragheh, Iran


Monitoring and assessment of surface water quality are very expensive and time consuming processes, thus finding cheap, simple and relatively exact methods which determine water quality class based on minimum parameters would be very useful. Decision tree as one of the data mining techniques classifies data sets based on a tree structure. In this study, the decision tree method was used to classify water quality in some hydrometric stations located at northern side of Sahand Mountain, including Bostanabad, PoleSenikh, Lighvan and Vanyar. The water quality classes were defined based on if-then rules. For every considered river, the discharge and 12 hydrochemical parameters, including Ca2+, Mg2+, Cl-, HCO3-, Na%, pH, SO42-, total anions, total cations, total dissolved solids (TDS), sodium adsorption ratio (SAR) and Electrical conductivity (EC) were collected and used for developing decision tree model. The results showed that the decision tree model could evaluate water quality class with high accuracy based on only four parameters: EC, pH, SAR and Na+. Moreover, the error of developed models in testing phase for Bostanabad, Vanyar, PoleSenikh and Lighvan stations were 3.4, 8.1, 22.9 and 1.6%, respectively.


Main Subjects

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Volume 4, Issue 2
June 2017
Pages 407-419
  • Receive Date: 25 December 2016
  • Revise Date: 12 March 2017
  • Accept Date: 15 March 2017
  • First Publish Date: 22 June 2017