GIS-based comparative characterization of groundwater quality of Tabas basin using multivariate statistical techniques and computational intelligence

  • A. AryafarEmail author
  • V. Khosravi
  • F. Hooshfar
Original Paper


Effective management of groundwater resources needs sustainable monitoring programs which are mainly performed based on water quality characterization. In the current research, hydrochemical characteristics of Tabas basin groundwater were analyzed by self-organizing map (SOM), multivariate statistical analysis and average groundwater quality index (AGWQI). Geographic information system was adopted to highlight the spatial variability of water indices, factors and clusters. AGWQI results show inappropriateness of groundwater for drinking purposes in some central and western parts of the study area (AGWQI > 100). A three-component model which explains over 80.75% of the total groundwater quality variations was suggested after factor analysis. Factor 1 (natural hydrochemical evolution of groundwater) includes high loadings of EC, TDS, TH, Ca2+ and Na+, Factor 2 (weathering and dissolution processes) includes high loadings of pH, Mg2+, HCO3 and depth, and Factor 3 (anthropogenic activities) includes high loadings of K+, Cl, SO42− and NO3. As the main goal of this study, groundwater data were also examined using SOM approach. Based on hydrochemical characteristics, groundwater samples were divided into three clusters. Cluster I containing 14% of groundwater samples (and sampling stations) is characterized by higher TDS, EC and TH values. Clusters II (characterized by higher Mg2+ concentration) and III (characterized by higher NO3 concentration) represent 50% and 36% of samples, respectively. Maps drawn show a meaningful compatibility among the spatial distribution of factors and clusters. This study proves that SOM can be successfully applied to characterize and classify groundwater in terms of quality on a regional scale.


Hydrochemistry Average groundwater quality index (AGWQI) Multivariate statistics Self-organizing map (SOM) GIS Tabas basin 



The authors would like to express their special thanks to University of Birjand for all support of the research. The help of Mr. Hassan Zia is also appreciated for providing hydrochemical data.

Compliance with ethical standards

Conflict of interest

The authors state they have no conflict of interest.


  1. Abrahart RJ, See LM, Solomatine DP (2008) Practical hydroinformatics: computational intelligence and technological developments in water applications. Springer, BerlinCrossRefGoogle Scholar
  2. Ahmed F, Fakhruddin A, Imam MT, Khan N, Abdullah ATM, Khan TA, Rahman MM, Uddin MN (2017) Assessment of roadside surface water quality of Savar, Dhaka, Bangladesh using GIS and multivariate statistical techniques. Appl Water Sci 7:3511–3525CrossRefGoogle Scholar
  3. Alhoniemi E, Hollmén J, Simula O, Vesanto J (1999) Process monitoring and modeling using the self-organizing map. Integr Comput-Aided Eng 6:3–14CrossRefGoogle Scholar
  4. Aryafar A, Ardejani FD (2009) Anisotropy and bedding effects on the hydro geological regime in a confined aquifer to design an appropriate dewatering system. Int J Environ Sci Technol 6:563–570CrossRefGoogle Scholar
  5. Aryafar A, Ardejani FD (2013) R-mod factor analysis, a popular multivariate statistical technique to evaluate water quality in Khaf-Sangan basin, Mashhad, northeast of Iran. Arab J Geosci 6:893–900CrossRefGoogle Scholar
  6. Ashrafzadeh A, Roshandel F, Khaledian M, Vazifedoust M, Rezaei M (2016) Assessment of groundwater salinity risk using kriging methods: a case study in northern Iran. Agric Water Manage 178:215–224CrossRefGoogle Scholar
  7. Banerjee T, Srivastava R (2010) Estimation of the current status of floral biodiversity at surroundings of integrated industrial Estate-Pantnagar, IndiaGoogle Scholar
  8. Basin PIARM (2014) Adaptive neuro fuzzy inference system for monthly groundwater level prediction in amaravathi river minor basin. J Theor Appl Inf Techno 68(3):523–530Google Scholar
  9. Berner RA (1987) Models for carbon and sulfur cycles and atmospheric oxygen; application to paleozoic geologic history. Am J Sci 287:177–196CrossRefGoogle Scholar
  10. Bowden GJ, Maier HR, Dandy GC (2002) Optimal division of data for neural network models in water resources applications. Water Resour Res 38:1–11CrossRefGoogle Scholar
  11. Bowers J, Shedrow C (2000) Predicting stream water quality using artificial neural networks. Misc Ser Westinghouse Savannah River Co, AikenGoogle Scholar
  12. Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index- do we dare? Water Sew Works 117(10):339–343Google Scholar
  13. Bu H, Tan X, Li S, Zhang Q (2010) Water quality assessment of the Jinshui river (China) using multivariate statistical techniques. Environ Earth Sci 60:1631–1639CrossRefGoogle Scholar
  14. Chen J-C, Chang N, Shieh W (2003) Assessing wastewater reclamation potential by neural network model. Eng Appl Artif Intell 16:149–157CrossRefGoogle Scholar
  15. Cheong J-Y, Hamm S-Y, Lee J-H, Lee K-S, Woo N-C (2012) Groundwater nitrate contamination and risk assessment in an agricultural area, South Korea. Environ Earth Sci 66:1127–1136CrossRefGoogle Scholar
  16. Choi B-Y, Yun S-T, Kim K-H, Kim J-W, Kim HM, Koh Y-K (2014) Hydrochemical interpretation of south korean groundwater monitoring data using self-organizing maps. J Geochem Explor 137:73–84CrossRefGoogle Scholar
  17. Das M, Kumar A, Mohapatra M, Muduli S (2010) Evaluation of drinking quality of groundwater through multivariate techniques in urban area. Environ Monit Assess 166:149–157CrossRefGoogle Scholar
  18. Davis JC (2002) Statistics and data analysis in geology. Wiley, HobokenGoogle Scholar
  19. Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108CrossRefGoogle Scholar
  20. De Rosemond S, Duro DC, Dubé M (2009) Comparative analysis of regional water quality in canada using the water quality index. Environ Monit Assess 156:223CrossRefGoogle Scholar
  21. Debels P, Figueroa R, Urrutia R, Barra R, Niell X (2005) Evaluation of water quality in the Chillán river (Central Chile) using physicochemical parameters and a modified water quality index. Environ Monit Assess 110:301–322CrossRefGoogle Scholar
  22. Dhanasekarapandian M, Chandran S, Devi DS, Kumar V (2016) Spatial and temporal variation of groundwater quality and its suitability for irrigation and drinking purpose using GIS and WQI in an urban fringe. J Afr Earth Sci 124:270–288CrossRefGoogle Scholar
  23. El Alfy M (2012) Integrated geostatistics and gis techniques for assessing groundwater contamination in Al Arish area, Sinai, Egypt. Arab J Geosci 5:197–215CrossRefGoogle Scholar
  24. Fulazzaky MA (2009) Water quality evaluation system to assess the Brantas river water. Water Resour Manag 23:3019CrossRefGoogle Scholar
  25. Giridharan L, Venugopal T, Jayaprakash M (2008) Evaluation of the seasonal variation on the geochemical parameters and quality assessment of the groundwater in the proximity of river Cooum, Chennai, India. Environ Monit Assess 143:161–178CrossRefGoogle Scholar
  26. Gnanachandrasamy G, Ramkumar T, Venkatramanan S, Vasudevan S, Chung S, Bagyaraj M (2015) Accessing groundwater quality in lower part of Nagapattinam district, Southern India: using hydrogeochemistry and GIS interpolation techniques. Appl Water Sci 5:39–55CrossRefGoogle Scholar
  27. Gorai AK, Kumar S (2013) Spatial distribution analysis of groundwater quality index using GIS: a case study of Ranchi Municipal Corporation (RMC) area. Geoinform Geostat Overv 1:2. Google Scholar
  28. Hamzaoui-Azaza F, Ketata M, Bouhlila R, Gueddari M, Riberio L (2011) Hydrochemical characteristics and assessment of drinking water quality in zeuss–koutine aquifer, southeastern tunisia. Environ Monit Assess 174:283–298CrossRefGoogle Scholar
  29. Hem JD (1985) Study and interpretation of the chemical characteristics of natural water. Department of the Interior, US Geological Survey, RestonGoogle Scholar
  30. Ishaku J, Ahmed A, Abubakar M (2011) Assessment of groundwater quality using chemical indices and GIS mapping in Jada area, Northeastern Nigeria. J Earth Sci Geotech Eng 1:35–60Google Scholar
  31. Jang CS, Chen SK, Ching-Chieh L (2008) Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan. Hydrol Process 22(22):4477–4489CrossRefGoogle Scholar
  32. Kalteh AM, Hjorth P, Berndtsson R (2008) Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ Model Software. 23:835–845CrossRefGoogle Scholar
  33. Karimi Bavandpur A, Hajihosaini A (2002) Geological map of Iran 1: 100,000 series-tabas. Geological Survey of Iran, IranGoogle Scholar
  34. Kohonen T (2001) Self-organizing maps. Springer, BerlinCrossRefGoogle Scholar
  35. Koklu R, Sengorur B, Topal B (2010) Water quality assessment using multivariate statistical methods—a case study: Melen river system (Turkey). Water Resour Manag 24:959–978CrossRefGoogle Scholar
  36. Kumar M, Ramanathan A, Rao M, Kumar B (2006) Identification and evaluation of hydrochemical processes in the groundwater environment of Delhi, India. Environ Geol 50:1025–1039CrossRefGoogle Scholar
  37. Kumar SK, Chandrasekar N, Seralathan P, Godson PS, Magesh N (2012) Hydrochemical study of shallow carbonate aquifers, Rameswaram Island, India. Environ Monit Assess 184:4127–4138CrossRefGoogle Scholar
  38. Kuo Y-M, Liu C-W, Lin K-H (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38:148–158CrossRefGoogle Scholar
  39. Kurunç A, Yürekli K, Cevik O (2005) Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey. Environ Model Software. 20:1195–1200CrossRefGoogle Scholar
  40. Lateef KH (2011) Evaluation of groundwater quality for drinking purpose for Tikrit and Samarra cities using water quality index. Eur J Sci Res 58:472–481Google Scholar
  41. Latha PS, Rao KN (2012) An integrated approach to assess the quality of groundwater in a coastal aquifer of Andhra Pradesh, India. Environ Earth Sci 66:2143–2169CrossRefGoogle Scholar
  42. Lawrence FW, Upchurch SB (1982) Identification of recharge areas using geochemical factor analysis. Groundwater 20:680–687CrossRefGoogle Scholar
  43. Lek S, Guégan J-F (1999) Artificial neural networks as a tool in ecological modelling, an introduction. Ecol Model 120:65–73CrossRefGoogle Scholar
  44. Li RZ (2006) Advance and trend analysis of theoretical methodology for water quality forecast. J Hefei Univ Technol 29:26–30Google Scholar
  45. Liu C-W, Lin K-H, Kuo Y-M (2003) Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci Total Environ 313:77–89CrossRefGoogle Scholar
  46. Magesh N, Chandrasekar N (2013) Evaluation of spatial variations in groundwater quality by WQI and GIS technique: a case study of Virudunagar district, Tamil Nadu, India. Arab J Geosci 6:1883–1898CrossRefGoogle Scholar
  47. Magesh N, Krishnakumar S, Chandrasekar N, Soundranayagam JP (2013) Groundwater quality assessment using WQI and GIS techniques, Dindigul district, Tamil Nadu, India. Arab J Geosci 6:4179–4189CrossRefGoogle Scholar
  48. Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32:1013–1022CrossRefGoogle Scholar
  49. Nasr M, Zahran HF (2014) Using of ph as a tool to predict salinity of groundwater for irrigation purpose using artificial neural network. Egypt J Aquat Res 40:111–115CrossRefGoogle Scholar
  50. Parizi HS, Samani N (2013) Geochemical evolution and quality assessment of water resources in the sarcheshmeh copper mine area (Iran) using multivariate statistical techniques. Environ Earth Sci 69:1699–1718CrossRefGoogle Scholar
  51. Prasanth SS, Magesh N, Jitheshlal K, Chandrasekar N, Gangadhar K (2012) Evaluation of groundwater quality and its suitability for drinking and agricultural use in the coastal stretch of Alappuzha district, Kerala, India. Appl Water Sci 2:165–175CrossRefGoogle Scholar
  52. Raju NJ, Shukla U, Ram P (2011) Hydrogeochemistry for the assessment of groundwater quality in varanasi: a fast-urbanizing center in Uttar Pradesh, India. Environ Monit Assess 173:279–300CrossRefGoogle Scholar
  53. Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manag 122:342–347CrossRefGoogle Scholar
  54. Rao NS (2006) Seasonal variation of groundwater quality in a part of Guntur district, Andhra Pradesh, India. Environ Geol 49:413–429CrossRefGoogle Scholar
  55. Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30:457–481CrossRefGoogle Scholar
  56. Saager R, Sinclair AJ (1974) Factor analysis of stream sediment geochemical data from the Mount Nansen area, Yukon Territory, Canada. Miner Deposita 9:243–252CrossRefGoogle Scholar
  57. Sadat-Noori S, Ebrahimi K, Liaghat A (2014) Groundwater quality assessment using the water quality index and GIS in Saveh-Nobaran Aquifer, Iran. Environ Earth Sci 71:3827–3843CrossRefGoogle Scholar
  58. Saeedi M, Abessi O, Sharifi F, Meraji H (2010) Development of groundwater quality index. Environ Monit Assess 163:327–335CrossRefGoogle Scholar
  59. Sami K (1992) Recharge mechanisms and geochemical processes in a semi-arid sedimentary basin, Eastern Cape, South Africa. J Hydrol 139:27–48. CrossRefGoogle Scholar
  60. Selvam S, Manimaran G, Sivasubramanian P, Balasubramanian N, Seshunarayana T (2014) Gis-based evaluation of water quality index of groundwater resources around Tuticorin coastal city, South India. Environ Earth Sci 71:2847–2867CrossRefGoogle Scholar
  61. Sidle W, Roose D, Shanklin D (2000) Isotopic evidence for naturally occurring sulfate pollution of ponds in the Kankakee River Basin, Illinois-Indiana. J Environ Qual 29:1594–1603CrossRefGoogle Scholar
  62. Singh KP, Basant A, Malik A, Jain G (2009) Artificial neural network modeling of the river water quality—a case study. Ecol Model 220:888–895CrossRefGoogle Scholar
  63. Skidmore A (2003) Environmental modelling with GIS and remote sensing. CRC Press, Boca RatonGoogle Scholar
  64. Srinivasamoorthy K, Vasanthavigar M, Vijayaraghavan K, Sarathidasan R, Gopinath S (2013) Hydrochemistry of groundwater in a coastal region of Cuddalore district, Tamilnadu, India: implication for quality assessment. Arab J Geosci 6:441–454CrossRefGoogle Scholar
  65. Štambuk-Giljanović N (1999) Water quality evaluation by index in dalmatia. Water Res 33:3423–3440CrossRefGoogle Scholar
  66. Stigter T, Ribeiro L, Dill AC (2006) Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies–two portuguese case studies. J Hydrol 327:578–591CrossRefGoogle Scholar
  67. Sunitha V, Sudarshan V, Reddy BR (2005) Hydrogeochemistry of groundwater, Gooty area, Anantapur district, Andhra Pradesh, India. Pollut Res 24:217Google Scholar
  68. Swarna Latha P (2008) Studies on spatial and temporal changes of land use and land cover, groundwater quality and shoreline of greater Visakhapatnam Municipal Corporation, Andhra Pradesh, India using remote sensing and GIS techniques. Unpublished Ph.D. thesis Andhra University, VisakhatapatnamGoogle Scholar
  69. Tiwari T, Mishra M (1985) A preliminary assignment of water quality index of major Indian rivers. Indian J Environ Prot 5:276–279Google Scholar
  70. Tiwari K, Goyal R, Sarkar A (2017) Gis-based spatial distribution of groundwater quality and regional suitability evaluation for drinking water. Environ Process 4:645–662CrossRefGoogle Scholar
  71. Vesanto J, Himberg J, Alhoniemi E, Parhankangas J (2000) Som toolbox for matlab 5. Helsinki University of Technology, EspooGoogle Scholar
  72. Wayland KG, Long DT, Hyndman DW, Pijanowski BC, Woodhams SM, Haack SK (2003) Identifying relationships between baseflow geochemistry and land use with synoptic sampling and r-mode factor analysis. J Environ Qual 32:180–190CrossRefGoogle Scholar
  73. Wen CG, Lee CS (1998) A neural network approach to multiobjective optimization for water quality management in a river basin. Water Resour Res 34:427–436CrossRefGoogle Scholar
  74. WHO (2011) Guidelines for drinking-water quality. World Health Organization, GenevaGoogle Scholar

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© Islamic Azad University (IAU) 2018

Authors and Affiliations

  1. 1.Department of Mining, Faculty of EngineeringUniversity of BirjandBirjandIran

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