Spatial Decision Support System for Groundwater Quality Management Using Geomatics

  • M. V. S. S. Giridhar
  • B. V. Nageswra Rao
  • G. K. Viswanadh
Part of the Water Science and Technology Library book series (WSTL, volume 84)


The quality of groundwater is very important in evaluating its utility for agriculture, domestic and industrial purposes. In the present study, it is proposed to develop ‘spatial decision support system (SDSS) for ground water quality management at village level using Geomatics’ for verifying the number of borewells required, based on the population in the village and their spatial distribution for the decision makers, NGOs, government authorities, etc. Further, the proposed SDSS will also help in carrying out the study and analysis of changes in water quality parameters with respect to pre- and post-monsoon seasons and their consequent impacts on human health. Keeping these points in view, a spatial decision support system (SDSS) has been formulated and developed in GIS environment for taking appropriate decisions for groundwater quality management at village level. Nandyal Mandal, Kurnool district, Andhra Pradesh, India, with 20 villages has been considered for this study. Nitrates and sulphates are present in 140 samples out of 310 samples collected in post-monsoon season data samples. These samples need to further laboratory analysis for calculating the concentration of nitrates and sulphates in all 140 samples located in various villages. The borewell having ID No. 133401332 located at DPEP School in Ayyalur village is having excess TDS, alkalinity, hardness, and presence of nitrates and sulphates, which shows that the borewell is not suitable for drinking purpose.


Groundwater GIS Nandyal Mandal Geomatics Fluorides Chlorides Total hardness TDS 


  1. Babiker IS, Mohammed MAA, Hiyama T, Kato K (2005) A GIS-based DRASTIC model for assessing aquifer vulnerability in Kakamigahara heights” Gifu Prefecture, central Japan. Sci Total Environ 345:127–140CrossRefGoogle Scholar
  2. Bonhamcarter GF (1996) Geographic information systems for geoscientists: modeling with GIS computer methods in the geosciences. Elsevier, Pergamon, vol 13, pp 1–50Google Scholar
  3. Chachadi AG, Lobo-Ferreira JP (2005) Assessing aquifer vulnerability to sea-water intrusion using GALDIT method: part 2—GALDIT indicator descriptions. IAHS and LNEC. In: Proceedings of the fourth inter celtic colloquium on hydrology and management of water resources. Universidade do Minho, Guimarães, PortugalGoogle Scholar
  4. Ckakraborthy S, Paul PK, Sikdar PK (2007) Assessing aquifer vulnerability to arsenic pollution using DRASTIC and GIS of North Bengal Plain: a case study of English Bazar Block, Malda District, West Bengal, India. J Spat Hydrol 7(1):101–121Google Scholar
  5. Connell LD, Van den Daele G (2003) A quantitative approach to aquifer vulnerability mapping. J Hydrol 276:71–88CrossRefGoogle Scholar
  6. Corwin DL, Vaughan PJ, Loague K (1997) Modeling nonpoint source pollutants in the vadose zone with GIS. Environ Sci Technol 31(8):2157–2175CrossRefGoogle Scholar
  7. Fuest S, Berlekamp J, Klein M, Matthies M (1998) Risk hazard mapping of groundwaterGoogle Scholar
  8. Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37:300–305Google Scholar
  9. Inhaber H (1975) An approach to a water quality index for Canada. Water Resour 9:821–833Google Scholar
  10. Maha ASMS, Al-Dabbagh (1989) Water quality index of groundwater wells at Horan area (western Iraq). Regional characterization of water quality. In: Proceedings of the Baltimore symposium IAHS publication no. 182, 1989, pp 99–108Google Scholar
  11. Phukon P, Phukan S, Das P, Sarma B (2004) Multicriteria evaluation in GIS environment for groundwater resource mapping in Guwahati City Areas, AssamGoogle Scholar
  12. Pradhan SK, Patnaik D, Rout SP (2001) Groundwater quality index for groundwater around a phosphatic fertilizers plant. Indian J Environ Prot 21(4):355–358Google Scholar
  13. Prati L, Pavanello R, Pesarin F (1971) Assessment of surface water quality by a single index of pollution. Water Resour 5:741–751Google Scholar
  14. Provencher M, Lamontagne J (1979) A method for establishing a water quality index for different uses. Quebec: Gouvernment du Quea’ bec, Ministea’ re des richesses naturelles, le Service de la qualitea’ des eaux, Bibliotequea’ nationale du Quea’ becGoogle Scholar
  15. Rajankar PN, Gulhane SR, Tambekar DH, Ramteke DS, Wate SR (2009) Water quality assessment of groundwater resources in Nagpur Region (India) based on WQI.
  16. Ramakrishnaiah CR, Sadashivaiah C, Ranganna G (2008) Assessment of water quality index for the groundwater in Tumkur Taluk, Karnataka State, India.
  17. Samake M, Tang Z, Hlaing W, M’Bue I, Kasereka K (2010) Assessment of groundwater pollution potential of the Datong basin, northern China. J Sustain Develop 3(2):140–152CrossRefGoogle Scholar
  18. Secunda S, Collin ML, Melloul AJ (1998) Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon Region. J Environ Manage 54:39–57CrossRefGoogle Scholar
  19. Shahid S, Nath SK (2000) GIS integration of remote sensing and electrical sounding data for hydrogeological exploration. J Spat Hydrol 2(1):1–12Google Scholar
  20. Sinha DK, Shrivastava AK (1994) Water quality index for river Sai at Rae Bareli for the pre monsoon period and after the onset of monsoon. Indian J Environ Prot 14(5):340–345Google Scholar
  21. Srinivasarao Y (2007) Groundwater quality suitable zones identification: application of GIS, Chittoor area, Andhra Pradesh. Environ Geol 53(1):201–210CrossRefGoogle Scholar
  22. Stigter TY, Ribeiro L, Carvalho Dill AMM (2006) Evaluation of an instrinsic and a specific vulnerability assessment method in comparison with groundwater salinisation and nitrate contamination levels in two agricultural regions in the south of Portugal. J Hydrogeol 14(1–2):79–99. CrossRefGoogle Scholar
  23. Tesoriero AJ, Inkpet EL, Voss FD (1998) Assessing groundwater vulnerability using logistic regression. In: Proceedings for the source water assessment and protection 98 conference, Dallas, TX, pp 157–165Google Scholar
  24. Zhang R, Hamerlinck JD, Gloss SP, Munn L (1996) Determination of non point-source pollution using GIS and numerical models. J Environ Qual 25:411–418CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • M. V. S. S. Giridhar
    • 1
  • B. V. Nageswra Rao
    • 1
  • G. K. Viswanadh
    • 2
  1. 1.Centre for Water ResourcesInstitute of Science and Technology, JNTUHHyderabadIndia
  2. 2.Civil EngineeringJNTUH College of Engineering, JNTUHHyderabadIndia

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