Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique

Aliasghar Azma, Esmaeil Narreie, Abouzar Shojaaddini, Nima Kianfar, Ramin Kiyanfar, Seyed Mehdi Seyed Alizadeh, Afshin Davarpanah

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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.

Original languageEnglish
Article number3788
Number of pages18
JournalSustainability (Switzerland)
Issue number7
Publication statusPublished - 29 Mar 2021


  • Carbonate aquifer
  • Digital elevation model
  • Groundwater quality assessment
  • Modeling
  • Multivariate linear regression
  • Principal component regression


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