Delineation of groundwater potential zones using remote sensing (RS), geographical information system (GIS) and analytic hierarchy process (AHP) techniques: a case study in the Leylia–Keynow watershed, southwest of Iran

  • H. R. Mohammadi-Behzad
  • A. Charchi
  • N. Kalantari
  • A. Mehrabi Nejad
  • H. Karimi Vardanjani
Original Article


In this research, a standard methodology has been applied to delineate groundwater resource potential zonation based on integrated remote sensing (RS), geographic information system (GIS), and analytical hierarchy process (AHP) techniques in Leylia–Keynow watershed, southwest of Iran. A total of five sets of criteria/factors (including lineament density, rainfall, lithology, slope, and drainage density) believed to be influencing groundwater storage potential in the area were selected. Each criterion/factor was assigned appropriate weight based on Saaty’s 9-point scale and the weights were normalized through the analytic hierarchy process (AHP). The process was integrated in the GIS environment to produce the groundwater potential prediction map for the area. The fi9-p groundwater prospect map obtained was classified as excellent potential, very good potential, good potential, moderate potential, and poor potential zone. The obtained results indicated that only 21% (122 km2) of the study area exhibit poor groundwater potential, whereas most of the regions (326 km2) in the research showed good to excellent groundwater potential. Also, about 24% (141 km2) was classified as having moderate groundwater potential. The good to excellent potential zones are characterized by the higher lineament density, higher rainfall, and lithology type such as limestone, whereas the poor to moderate groundwater potential zones are characterized by the lesser lineament density, lower rainfall, lithology type of shale and marl as well as shale and limestone. Based on the obtained evidences, the tectonic structures had an important role in fracturing and crushing of limestone units in the area and so are vital for karst and ground water sources development. The demarcation of groundwater potential zones in the Leylia–Keynow watershed will be helpful for future planning, development and management of the groundwater resources.


Groundwater potential zone map Remote sensing GIS AHP Leylia–Keynow watershed 



The authors gratefully acknowledge the facilities provided by the Chairman, Department of Geology, Shahid Chamran University of Ahvaz.


  1. Abel OT, Tijani M (2011) Integrated remote sensing and GIS approach to groundwater potential assessment in the basement terrain of Ekiti area southwestern Nigeria. Mater Geoenviron 58(3):303–328Google Scholar
  2. Adiat KAN, Nawawi MNM, Abdullah K (2012) Assessing the accuracy of GIS-based elementary multi criteria decision analysis as a spatial prediction tool–A case of predicting potential zones of sustainable groundwater resources. J Hydrol 440:75–89CrossRefGoogle Scholar
  3. Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36CrossRefGoogle Scholar
  4. Arkoprovo B, Adarsa J, Shashi Prakash S (2012) Delineation of groundwater potential zones using satellite remote sensing and geographic information techniques: a case study from Ganjam district, Orissa, India. Res J Recent Sci 9:59–66Google Scholar
  5. Bera K, Bandyopadhyay J (2012) Groundwater potential mapping in Dulung watershed using remote sensing and GIS techniques, West Bengal, India. Int J Sci Res Publ 2(12):1–7Google Scholar
  6. Chandio IA, Matori ANB, WanYusof KB, Talpur MAH, Balogun AL, Lawal DU (2013) GIS-based analytic hierarchy process as a multicriteria decision analysis instrument: a review. Arab J Geosci 6(8):3059–3066CrossRefGoogle Scholar
  7. Chitsazan M, Vardanjani HK, Karimi H, Charchi A (2015) A comparison between karst development in two main zones of Iran: case study—Keyno anticline (Zagros Range) and Shotori anticline (Central Iran). Arab J Geosci 8(12):10833–10844Google Scholar
  8. Chowdary VM, Chakraborthy D, Jeyaram A, Krishna Murthy YVN, Sharma JR, Dadhwal VK (2013) Multi-criteria decision making approach for watershed prioritization using analytic hierarchy process technique and GIS. Water Resour Manag 27:3555–3571CrossRefGoogle Scholar
  9. Chowdhury A, Jha MK, Chowdary VM (2010) Delineation of groundwater recharge zones and identification of artificial recharge sites in West Medinipur District, West Bengal using RS, GIS and MCDM techniques. Environ Earth Sci 59(6):1209–1222CrossRefGoogle Scholar
  10. Chung CJF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472CrossRefGoogle Scholar
  11. Deepika B, Avinash K, Jayappa KS (2013) Integration of hydrological factors and demarcation of groundwater prospect zones: insights from remote sensing and GIS techniques. Environ Earth Sci 70(3):1319–1338CrossRefGoogle Scholar
  12. Dinesh Kumar PK, Gopinath G, Seralathan P (2007) Application of remote sensing and GIS for the demarcation of groundwater potential zones of a river basin in Kerala, southwest cost of India. Int J Remote Sens 28(24):5583–5601CrossRefGoogle Scholar
  13. Dunning DJ, Ross QE, Merkhofer MW (2000) Multiattribute utility analysis for addressing Section 316 (b) of the Clean Water Act. Environ Sci Policy 3:7–14CrossRefGoogle Scholar
  14. Eastman JR (2003) IDRISI Kilimanjaro: guide to GIS and image processing. Clark Labs, Clark University, Worcester, pp 328Google Scholar
  15. Edet AE, Okereke CS, Teme SC, Esu EO (1998) Application of remote-sensing data to groundwater exploration: a case study of the Cross River State, southeastern Nigeria. Hydrogeol J 6:394–404CrossRefGoogle Scholar
  16. Ettazarini S (2007) Groundwater potential index: a strategically conceived tool for water research in fractured aquifers. Environ Geol 52:477–487CrossRefGoogle Scholar
  17. Flug M, Seitz HLH, Scott JF (2000) Multicriteria decision analysis applied to Glen Canyon Dam. J Water Resour Plan Manage ASCE 126(5):270–276CrossRefGoogle Scholar
  18. Gitas IZ, Ayanz JSM, Chuvieco BDE, Camia A (2014) Advances in remote sensing and GIS applications in support of forest fire management. Int J Wildland Fire 23:603–605CrossRefGoogle Scholar
  19. Gowd SS (2004) Electrical resistivity survey to delineate groundwater potential aquifers in Peddavanka watershed, Anantapur District, Andhra Pradesh, India. Environ Geol 46:118–131Google Scholar
  20. Hajkowicz S, Higgins A (2008) A comparison of multiple criteria analysis techniques for water resource management. Eur J Oper Res 184:255–265CrossRefGoogle Scholar
  21. Israil M, Al-hadithi M, Singhal DC (2006) Application of a resistivity survey and geographical information system (GIS) analysis for hydrogeological zoning of a piedmont area, Himalayan foothill region, India. Hydrogeol J 14:753–759CrossRefGoogle Scholar
  22. Jha MK, Chowdhury A, Chowdary VM, Peiffer S (2007) Groundwater management and development by integrated remote sensing and geographic information systems: prospects and constraints. Water Resour Manag 21(2):427–467CrossRefGoogle Scholar
  23. Jha MK, Chowdary VM, Chowdhury A (2010) Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques. Hydrogeol J 18(7):1713–1728CrossRefGoogle Scholar
  24. Joubert A, Stewart TJ, Eberhard R (2003) Evaluation of water supply augmentation and water demand management options for the City of Cape Town. J Multi-Criteria Decis Anal 12(1):17–25CrossRefGoogle Scholar
  25. Junge B, Alabi T, Sonder K, Marcus S, Abaidoo R, Chikoye D, Stahr K (2010) Use of remote sensing and GIS for improved natural resources management: case study from different agroecological zone of West Africa. Int J Remote Sens 31:5116–6141CrossRefGoogle Scholar
  26. Kaliraj S, Chandrasekar N, Magesh NS (2014) Identification of potential groundwater recharge zones in Vaigai upper basin, Tamil Nadu, using GIS-based analytical hierarchical process (AHP) technique. Arab J Geosci 7:1385–1401CrossRefGoogle Scholar
  27. Kumar T, Gautam AK, Kumar T (2014) Appraising the accuracy of GIS multi-criteria decision making technique for delineation of groundwater potential zone. Water Resour Manage 28:4449–4466CrossRefGoogle Scholar
  28. Machiwal D, Jha MK, Mal BC (2011) Assessment of groundwater potential in a Semi-Arid region of india using remote sensing, GIS and MCDM techniques. Water Resour Manage 25:1359–1386CrossRefGoogle Scholar
  29. Madrucci V, Taioli F, Cesar De Araujo C (2008) Groundwater favorability map using GIS multicriteria data analysis on crystalline terrain, Sao Paulo State, Brazil. J Hydrol 357:153–173CrossRefGoogle Scholar
  30. Magesh NS, Chandrasekar N, Soundranayagam JP (2012) Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing. GIS and MIF techniques. Geosci Front 3(2):189–196CrossRefGoogle Scholar
  31. Malczewski J (1999) GIS and Multicriteria decision analysis. Wiley, New YorkGoogle Scholar
  32. Mallick J, Singh CK, Al-Wadi H, Ahmed M, Rahman A, Shashtri S, Mukherjee S (2014) Geospatial and geostatistical approach for groundwater potential zone delineation. Hydrol Processes. Google Scholar
  33. Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N (2011) A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arab J Geosci 6:1621–1637CrossRefGoogle Scholar
  34. Mogaji KA, Lim HS, Abdullah K (2014) Regional prediction of groundwater potential mapping in a multifaceted geology terrain using GIS-based Dempster-Shafer model. Arab J Geosci. Google Scholar
  35. Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2013) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan watershed Iran. Arab J Geosci. Google Scholar
  36. Mohammadi-Behzad HR (2016) Investigation of recharge sources karstic aquifers by using physico-chemical parameters and stable isotopes (18O and 2H) in NE Khuzestan. Unpublished PhD thesis, Ahvaz, University of Shahid Chamran, p 222Google Scholar
  37. Muheeb MA, Rasheed AJ (2009) Evaluation of aquifers vulnerability to contamination in the Yarmouk river watershed, Jordan, based on DRASTIC method. Arab J Geosci 3:273–282Google Scholar
  38. Murthy KSR, Mamo AG (2009) Multi-criteria decision evaluation in groundwater zones identification in Moyale-Teltele subbasin, South Ethiopia. Int J Remote Sens 30:2729–2740CrossRefGoogle Scholar
  39. Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300CrossRefGoogle Scholar
  40. Pandian M, Kumanan CJ (2013) Geomatics approach to demarcate groundwater potential zones using remote sensing and GIS techniques in part of Trichy and Karur district, Tamilnadu, India. Appl Water Sci 5(2):234–240Google Scholar
  41. Pietersen K (2006) Multiple criteria decision analysis (MCDA): a tool to support sustainable management of groundwater resources in South Africa. Water SA 32(2):119–128Google Scholar
  42. Pinto D, Shrestha S, Babel MS, Ninsawat S (2015) Delineation of groundwater potential zones in the Comoro watershed, Timor Leste using GIS, remote sensing and analytic hierarchy process (AHP) technique. Appl Water Sci. Google Scholar
  43. Pourghasemi HR, Moradi HR, Fatemi Aghda SM, Gokceoglu C, Pradhan B (2012) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 7(5):1857–1878CrossRefGoogle Scholar
  44. Pradhan B (2009) Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Cent Eur J Geosci 1(1):120–129Google Scholar
  45. Prasad RK, Mondal NC, Banerjee P, Nanda Kumar MV, Singh VS (2008) Deciphering potential groundwater zone in hard rock through the application of GIS. Environ Geol 55:467–475CrossRefGoogle Scholar
  46. Rahmati O, Nazari Samani A, Mahdavi M, Pourghasemi HR, Zeinivand H (2014) Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci. Google Scholar
  47. Rao YS, Jugran DK (2003) Delineation of groundwater potential zones and zones of groundwater quality suitable for domestic purposes using remote sensing and GIS. Hydrol Sci J 48(5):821–833CrossRefGoogle Scholar
  48. Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw-Hill, New YorkGoogle Scholar
  49. Saaty TL (1986) Axiomatic foundation of the analytic hierarchy process. Manage Sci 32:841–855CrossRefGoogle Scholar
  50. Saaty TL (1992) The hierarchon: a dictionary of hierarchies. RWS Publications, Pittsburgh, p 496Google Scholar
  51. Sener E, Davraz A, Ozcelik M (2005) An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey. Hydrogeol J 13(5–6):826–834CrossRefGoogle Scholar
  52. Shahid S, Nath SK, Roy J (2000) Groundwater potential modeling in a softrock area using a GIS. Int J Remote Sens 21(9):1919–1924CrossRefGoogle Scholar
  53. Shekhar S, Pandey AC (2014) Delineation of groundwater potential zone in hard rock terrain of India using remote sensing, geographical information system (GIS) and analytic hierarchy process (AHP) techniques. Geocarto Int 30(4):402–442CrossRefGoogle Scholar
  54. Solomon S, Quiel F (2006) Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeol J 14:729–741CrossRefGoogle Scholar
  55. Sree Devi PDS, Srinivasulu S, Raju KK (2001) Hydrogeomorphological and groundwater prospects of the Pageru river basin by using remote sensing data. Environ Geol 40:1088–1094CrossRefGoogle Scholar
  56. Srivastava PK, Bhattacharya AK (2006) Groundwater assessment through an integrated approach using remote sensing, GIS and resistivity techniques: a case study from a hard rock terrain. Int J Remote Sens 27(20):4599–4620CrossRefGoogle Scholar
  57. Subba Rao N, Chakradhar GKJ, Srinivas V (2001) Identification of groundwater potential zones using remote sensing techniques in and around Gunur town, Andhra Pradesh, India. J Indian Soc Remote Sens 29:69–78CrossRefGoogle Scholar
  58. Teeuw RM (1995) Groundwater exploration using remote sensing and a low-cost geographical information system. Hydrogeol J 3:21–30CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • H. R. Mohammadi-Behzad
    • 1
  • A. Charchi
    • 1
  • N. Kalantari
    • 1
  • A. Mehrabi Nejad
    • 1
  • H. Karimi Vardanjani
    • 1
  1. 1.Department of Geology, Faculty of Earth SciencesShahid Chamran University of AhvazAhvazIran

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