Groundwater quality assessment is vital to protect this resource. Therefore, the aims of this study were to evaluate the hydro-chemical quality of the Marvdasht aquifer located in the semi-arid region of Iran and to map the groundwater quality parameters. For this purpose, a mean data of 11 groundwater quality parameters collected from 49 wells (2010–2015) were used. Pie, Schoeller and Piper diagrams were used to determine the dominant ions and type of water. Ion ratios and Gibbs diagrams were used to illustrate the chemistry and processes in the groundwater. Spatial distribution of quality parameters were mapped using ArcGIS. Results showed that the water type is Na-Cl and Cl− with abundance orders of CL− > SO42− > HCO3− and Na+ with abundance orders of Na+ > Mg2 + >Ca2+ > K+ are dominant anion and cation, respectively. Gibbs diagrams revealed that geological formations control the groundwater chemistry in 66% of the groundwater samples. Based on the Wilcox diagram, only 24% of the samples fell into the C4–S4 class with high salinity and alkalinity hazard. The maps showed that generally groundwater in the north of the study site has better quality than that the south of the study site, where the existence of dolomite and chalky formations leads to decreasing water quality.

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In arid and semi-arid lands like Iran water is scarce, and not all the wastewater can be treated. Hence, groundwater remains the primary and the principal source of water supply for human consumption. Therefore, this study attempted to spatially assess the groundwater potential in an aquifer in a semi-arid region of Iran using geographic information systems (GIS)-based statistical modeling. To this end, 75 agricultural wells across the Marvdasht Plain were sampled, and the water samples’ electrical conductivity (EC) was measured. To model the groundwater quality, multiple linear regression (MLR) and principal component regression (PCR) coupled with elven environmental parameters (soil-topographical parameters) were employed. The results showed that that soil EC (SEC) with Beta = 0.78 was selected as the most influential factor affecting groundwater EC (GEC). CaCO3 of soil samples and length-steepness (LS factor) were the second and third effective parameters. SEC with r = 0.89 and CaCO3 with r = 0.79 and LS factor with r = 0.69 were also characterized for PC1. According to performance criteria, the MLR model with R2 = 0.94, root mean square error (RMSE) = 450 µScm−1 and mean error (ME) = 125 µScm−1 provided better results in predicting the GEC. The GEC map indicated that 16% of the Marvdasht groundwater was not suitable for agriculture. It was concluded that GIS, combined with statistical methods, could predict groundwater quality in the semi-arid regions.