Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.

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Increasing population, high demand for food, and uncontrolled abstraction of aquifers have severely affected the water quality. This study aimed to evaluate the water quality of 17 deep agricultural wells in Bahabad plain from the perspective of irrigation and drinking. In order to determine the water quality of wells and analyze the water quality index (WQI), a set of statistical methods such as Fuzzy Analytic Hierarchy Process (FAHP) and TOPSIS were used. WQI is considered one of the primary methods for assessing drinking water quality. Still, due to the discrepancy between the results and the WQI(WHO), it was decided to modify the WQI method. The integrated use of FAHP-WQI and the TOPSIS method led to significant changes in the grading and the classification of water wells. The results showed that these two methods combined could be used as a good and complementary technique to eliminate ranking inconsistencies by WQI. Combining WQI results with GIS also allows for a deeper analysis of drinking water quality. The results showed that most of the water quality problems are due to wells in the northern region of the plain, and more than 41% of wells in this region are not in good condition.

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