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Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique

Author

Listed:
  • Aliasghar Azma

    (College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China)

  • Esmaeil Narreie

    (Department of Surveying Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman 76311-33131, Iran)

  • Abouzar Shojaaddini

    (Soil Science Department, College of Agriculture, Tarbiat Modares University, Tehran 14115-336, Iran)

  • Nima Kianfar

    (Faculty of Geodesy & Geomatics Engineering, K. N. Toosi University of Technology, Tehran 15875-4416, Iran)

  • Ramin Kiyanfar

    (Department of Art and architecture, Payame Noor University, Shiraz 19395-4697, Iran)

  • Seyed Mehdi Seyed Alizadeh

    (Petroleum Engineering Department, Australian College of Kuwait, West Mishref 13015, Kuwait)

  • Afshin Davarpanah

    (Department of Mathematics, Aberystwyth University, Aberystwyth SY23 3FL, UK)

Abstract

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). CaCO 3 of soil samples and length-steepness (LS factor) were the second and third effective parameters. SEC with r = 0.89 and CaCO 3 with r = 0.79 and LS factor with r = 0.69 were also characterized for PC1. According to performance criteria, the MLR model with R 2 = 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.

Suggested Citation

  • Aliasghar Azma & Esmaeil Narreie & Abouzar Shojaaddini & Nima Kianfar & Ramin Kiyanfar & Seyed Mehdi Seyed Alizadeh & Afshin Davarpanah, 2021. "Statistical Modeling for Spatial Groundwater Potential Map Based on GIS Technique," Sustainability, MDPI, vol. 13(7), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:7:p:3788-:d:526203
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    References listed on IDEAS

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    1. Saro Lee & Chang-Wook Lee, 2015. "Application of Decision-Tree Model to Groundwater Productivity-Potential Mapping," Sustainability, MDPI, vol. 7(10), pages 1-17, September.
    2. Lazhar Belkhiri & Tahoora Narany, 2015. "Using Multivariate Statistical Analysis, Geostatistical Techniques and Structural Equation Modeling to Identify Spatial Variability of Groundwater Quality," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2073-2089, April.
    3. Sunmin Lee & Yunjung Hyun & Moung-Jin Lee, 2019. "Groundwater Potential Mapping Using Data Mining Models of Big Data Analysis in Goyang-si, South Korea," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
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