Application of CART decision tree data mining to determine the most effective drinking water quality factors (case study: Kazeroon plain, Fars province)

• Main Authors: SM Soleimanpour, SH Mesbah, B Hedayati
• Format: Article
• Language: fas
• Published: Tehran University of Medical Sciences 2018-06-01
• Series: سلامت و محیط
• Subjects: drinking water; data mining; decision tree; kazeroon plain; water quality
• Online Access: http://ijhe.tums.ac.ir/article-1-5881-en.html

Background and Objective: Determination of quality parameters of drinkable water is important, especially in developing countries, to increase the productivity and better management and planning of water resources. The aim of current study was to apply CART decision tree data mining technique to determine the most effective factors on drinkable water quality in Kazeroon plain, located west of Fars province, Iran. Materials and Methods: Qualitative parameters of 60 drinkable wells such as SAR, Na, Cl, SO4, TH, TDS, pH, NO3, CaCO3, HCO3, Ca, Mg, K and EC were taken in the study area. The most effective factors on quality of drinkable water were determined with 90% accuracy, using CART decision tree data mining technique in Clementine 12.0 software. Results: The results showed that total dissolved solids (TDS) and calcium content (Ca) had the highest impact on quality of drinking water. Therefore, when the TDS of water in this plain is equal or less than 495 mg/L and the calcium content is equal or less than 6.150 meq/L, the water is suitable for drinking. Conclusion: The TDS and Ca content were the most effective parameters on the quality of drinkable water in this plain, due to its geological formation and the existence of CaCO3 in its structure. The water purification, reduction of soluble material concentration, and monitoring of wells in this plain are recommended.