Spatial mapping of groundwater potential in Ponnaiyar River basin using probabilistic-based frequency ratio model

  • A. Jothibasu
  • S. Anbazhagan
Original Article


Water is a precious natural resource without it life is not possible. The demand for water has rapidly increased over the last few years and this has resulted in water scarcity in many parts of the world. The main aim of this study is to examine the application of the probabilistic-based frequency ratio (FR) model in groundwater potential mapping at Ponnaiyar River basin in Tamil Nadu, India. In the present study includes the analysis of the spatial relationships between groundwater yield and various hydrological conditioning factors such as altitude, slope angle, curvature, drainage, lineament, lithology, soil depth, and land use/land cover for this region. The eight groundwater conditioning factors were collected and extracted from topographic data, geological data, satellite imagery, and published maps. Then, the 74 groundwater data with high potential yield values of ≥40 m3/h were collected and mapped in GIS. Out these, 44 (60%) cases were randomly selected for models training, and the remaining 31 (40%) cases were used for the validation purposes. Finally, the frequency ratio coefficients of the hydrological factors were used to generate the groundwater potential map. The receiver operating characteristic (ROC) curve was drawn for groundwater potential map, and the area under curve (AUC) was computed. Results indicated that the rainfall and slope percent factors have taken the highest and lowest weights, respectively. Validation of results showed that the FR method (AUC = 78.90%) performed fairly good predication accuracy. Results of this study could be helpful for better management of groundwater resources in the study area and give planners and decision makers an opportunity to prepare appropriate groundwater investment plans for sustainable environment.


Geographic information system Frequency ratio Groundwater potential Ponnaiyar River India 



The authors thank the anonymous reviewers for their valuable comments and suggestions to improve the content of the article.


  1. Adiat K, 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
  2. Al-Abadi AM (2015) Groundwater potential mapping at northeastern Wasit and Missan governorates, Iraq using a data-driven weights of evidence technique in framework of GIS. Environ Earth Sci 74(2):1109–1124CrossRefGoogle Scholar
  3. Al Saud M (2010) Mapping potential areas for groundwater storage in wadi aurnah basin, western Arabian peninsula, using remote sensing and geographic information system techniques. Hydrogeol J 18:1481–1495CrossRefGoogle Scholar
  4. Alavi M (1994) Tectonics of the Zagros orogenic belt of Iran; new data and interpretations. Tectonophysics 229:211–238CrossRefGoogle Scholar
  5. Anbazhagan S (2004) Geoinformatics for Hydrological studies. In: Proceedings of regional colloquium an industry-academia meet on advanced materials. Biosciences, and Computers and Information Technology, Daman, pp 25–27Google Scholar
  6. Anbazhagan S, Jothibasu A (2015) Assessment of hydroclimatic condition in extensive groundwater mining area, Southern India. J Ind Geophy Union 19:302–312Google Scholar
  7. Anbazhagan S, Ramasamy SM, Moses Edwin J (2000) Remote sensing and geophysical resistivity survey for groundwater exploration—a comparative analysis. In: Conference on groundwater exploration techniques, Tiruchirappalli, pp 177–181Google Scholar
  8. Anbazhagan S, Aschenbrenner F, Knoblich K (2001) Comparison of aquifer parameters with lineaments derived from remotely sensed data in kinzig basin. In: Congress XXXI. IAH (ed): new approaches to characterizing groundwater flow, vol 2. Germany, Munich, pp 883–886Google Scholar
  9. Aniya M (1985) Landslide-susceptibility mapping in the amahata river basin, Japan. Ann Assoc Am Geogr 75(1):102–114CrossRefGoogle Scholar
  10. 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
  11. Ayazi MH, Pirasteh S, Arvin AKP, Pradhan B, Nikouravan B, Mansor S (2010) Disasters and risk reduction in groundwater: Zagros mountain southwest Iran using geo-informatics techniques. Dis Adv 3(1): 51–57Google Scholar
  12. Baghvand A, Nasrabadi T, Bidhendi GN, Vosoogh A, Karbassi A, Mehrdadi N (2010) Groundwater quality degradation of an aquifer in Iran central desert. Desalination 260:264–275CrossRefGoogle Scholar
  13. Bandyopadhyay S, Srivastava SK, Jha MK, Hegde VS, Jayaraman V (2007) Harnessing earth observation (EO) capabilities in hydrogeology: an Indian perspective. Hydrogeol J 15(1):155–158CrossRefGoogle Scholar
  14. Banks D, Robins N (2002) An introduction to groundwater in crystalline bedrock. Norges geologiske undersøkelse, Trondheim, p 64Google Scholar
  15. Bastanim, Kholghi M, Rakhshandehroo GR (2010) Inverse modeling of variable-density groundwater flow in a semi-arid area in Iran using a genetic algorithm. Hydrogeol J 18:1191–1203CrossRefGoogle Scholar
  16. Bevan Mj, Endres AL, Rudolph DL, Parkin G (2005) A field scale study of pumping-induced drainage and recovery in an unconfined aquifer. J Hydrol 315:52–70CrossRefGoogle Scholar
  17. Brunner P, Bauer P, Eugster M, Kinzelbach W (2004) Using remote sensing to regionalize local rainfall recharge rates obtained from the chloride method. J Hydrol 294(4):241–250CrossRefGoogle Scholar
  18. Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at Hoa binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211CrossRefGoogle Scholar
  19. Central Groundwater Board (CGWB) (2012) Yearly reportGoogle Scholar
  20. Charon JE (1974) Hydrogeological applications of ERTS satellite imagery. In: Proc UN/FAO regional seminar on remote sensing of earth resources and environment. Commonwealth Science Council, Cairo, pp 439–456Google Scholar
  21. Chenini I, Mammou AB, May MY (2010) Groundwater recharge zone mapping using GIS-based multi-criteria analysis: a case study in central Tunisia (maknassy basin). Water ResourManag 24:921–939Google Scholar
  22. Chowdhury A, Jha MK, Chowdary VM, Mal BC (2009) Integrated remote sensing and GIS-based approach for assessing groundwater potential in west medinipur district, west Bengal, India. Int J Remote Sens 30(1):231–250CrossRefGoogle Scholar
  23. Chung JF, Fabbri AG (2003) Validation of spatial prediction models for landslide hazard mapping. Nat Hazards 30(3):451–472CrossRefGoogle Scholar
  24. Co RM (1990) Handbook of groundwater development. Wiley, New York, pp 34–51Google Scholar
  25. Dar IA, Sankar K, Dar MA (2010) Remote sensing technology and geographic information system modeling: an integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin. J Hydrol 394:285–295CrossRefGoogle Scholar
  26. Davoodi Moghaddam D, 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. doi: 10.1007/s12517-013-1161-5 Google Scholar
  27. 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
  28. E.C. Inc. (Expert Choice Inc.) (1995) Decision support software: tutorial, expert choice, Student Version 9. Expert Choice Inc., PittsburghGoogle Scholar
  29. Elewa HH, Qaddah AA (2011) Groundwater potentiality mapping in the Sinai peninsula, Egypt, using remote sensing and GIS-watershedbased modeling. Hydrogeol J 19:613–628CrossRefGoogle Scholar
  30. Ettazarini S (2007) Groundwater potential index: a strategically conceived tool for water research in fractured aquifers. Environ Geol 52:477–487CrossRefGoogle Scholar
  31. Ettazarizini S, El Mahmouhi N (2004) Vulnerability mapping of the turonian limestone aquifer in the phosphate plateau (Morocco). Environ Geol 46:113–117Google Scholar
  32. Faust N, Anderson WH, Star JL (1991) Geographic information systems and remote sensing future computing environment. Photogram Eng Remote Sens 57(6):655–668Google Scholar
  33. Florinsky IV (2000) Relationships between topographically expressed zones of flow accumulation and sites of fault intersection: analysis by means of digital terrain modelling. Environ Model Softw 15(1):87–100CrossRefGoogle Scholar
  34. Gaur S, Chahar BR, Graillot D (2011) Combined use of groundwater modeling and potential zone analysis for management of groundwater. Int J Appl Earth Obs 13:127–139CrossRefGoogle Scholar
  35. Geological Survey of India (GSI) (1998) reportGoogle Scholar
  36. Goodchild MF (1993) The state of GIS for environmental problemsolving. In: Goodchild MF, Parks BO, Steyaert LT (eds) Environmental modeling with GIS. Oxford University Press, New York, pp 8–15Google Scholar
  37. Hinton JC (1996) GIS and remote sensing integration for environmental applications. Int J Geograp Inf syst 10(7):877–890Google Scholar
  38. Hobbs WH (1904) Lineaments of the Atlantic border region. Geol Soc Am Bull 15(1):483–506CrossRefGoogle Scholar
  39. Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A (2014) GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Te. doi: 10.1007/s13762-013-0464-0 Google Scholar
  40. 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
  41. Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. Earth Sys Sci 115(6):661–667CrossRefGoogle Scholar
  42. 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 Manag 25:1359–1386CrossRefGoogle Scholar
  43. Madan KJ, Chowdary VM, Chowdhury A (2010) Groundwater assessment in salboni block, west Bengal (India) using remote sensing, geographical information systemand multi-criteria decision analysis techniques. Hydrogeol J 18:1713–1728CrossRefGoogle Scholar
  44. Madrucci V, Taioli F, Cesar De Araujo C (2008) Groundwater favorability map using GIS multi criteria data analysis on crystalline terrain, Sao Paulo State, Brazil. J Hydrol 357:153–173CrossRefGoogle Scholar
  45. 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
  46. Malczewski J (1999) GIS andMulticriteria decision analysis. JohnWiley and Sons, Inc, United States of America, pp 177–192Google Scholar
  47. Manap MA, Sulaiman WNA, Ramli MF, Pradhan B, Surip N (2013) A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia. Arab J Geosci 6:1621–1637CrossRefGoogle Scholar
  48. Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF (2014) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci 7:711–724CrossRefGoogle Scholar
  49. 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. doi: 10.1007/s12517-014-1391-1 Google Scholar
  50. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at golestan province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidencemodels. J Asian Earth Sci 61:221–236CrossRefGoogle Scholar
  51. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modeling: a review of hydrological, geomorphological and biological applications. Hydrol Process 5:3–30CrossRefGoogle Scholar
  52. Mukherjee P, Singh CK, Mukherjee S (2012) Delineation of groundwater potential zones in arid region of India—a remote sensing and GIS approach. Water Resour Manag 26:2643–2672CrossRefGoogle Scholar
  53. Musaka, Akhir JM, Abdullah I (2000) Groundwater prediction potential zone in Langat Basin using the integration of remote sensing and GIS. Accessed 24 Jul 2008Google Scholar
  54. Naghibi SA, Pourghasemi HR, Pourtaghie ZS, Rezaei A (2014) Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the moghan watershed. Iran Earth Sci Inform. doi: 10.1007/s12145-014-0145-7 Google Scholar
  55. 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
  56. Nazari A, Salarirad MM, Aghajani Bazzazi A (2012) Landfill site selection by decision-making tools based on fuzzy multi attribute decision-making method. Environ. Earth Sci 65:1631–1642CrossRefGoogle Scholar
  57. Negnevitsky M (2002) Artificial intelligence: a guide to intelligent systems. Addison–Wesley/Pearson, Harlow, p 394Google Scholar
  58. Neshat A, Pradhan B, Pirasteh S, Shafri HZM (2013) Estimating groundwater vulnerability to pollution using modified DRASTIC model in the Kerman agricultural area Iran. Environ Earth Sci. doi: 10.1007/s12665-013-2690-7 Google Scholar
  59. Nosrati K, Eeckhaut MVD (2012) Assessment of groundwater quality using multivariate statistical techniques in hashtgerd plain, Iran. Environ Earth Sci 65:331–344CrossRefGoogle Scholar
  60. Oh HJ, Kim YS, Choi JK, Park E, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399:158–172CrossRefGoogle Scholar
  61. Ozdemir A (2011a) GIS-based groundwater spring potential mapping in the Sultan Mountains (Konya, Turkey) using frequency ratio, weights of evidence and logistic regression methods and their comparison. J Hydrol 411(3–4):290–308CrossRefGoogle Scholar
  62. Ozdemir A (2011b) Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the sultan mountains (Aksehir, Turkey). J Hydrol 405(1):123–136CrossRefGoogle Scholar
  63. Page ML, Berjamy B, Fakir Y, Bourgin F, Jarlan J, Abourida A, Benrhanem M, Jacob G, Huber M, Sghrer F, Simonneaux V, Chehbouni G (2012) An integrated DSS for groundwater management based on remote sensing. the case of a semi-arid aquifer in Morocco. Water Resour Manag 26:3209–3230CrossRefGoogle Scholar
  64. Pourghasemi HR, Pradhan B, Gokceoglu C (2012a) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996CrossRefGoogle Scholar
  65. Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Remote sensing data derived parameters and its use in landslide susceptibility assessment using Shannon’s Entropy and GIS. Appl. Mech Mater 225:486–491CrossRefGoogle Scholar
  66. Pourtaghi ZS, Pourghasemi HR (2014) GIS-based groundwater spring potential assessment and mapping in the Birjand Township, southern Khorasan Province Iran. Hydrogeol J. doi: 10.1007/s10040-013-1089-6 Google Scholar
  67. 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
  68. Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365CrossRefGoogle Scholar
  69. Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30CrossRefGoogle Scholar
  70. Prasad RK, Mondal NC, Banerjee P, Nandakumar MV, Singh VS (2008) Deciphering potential groundwater zone in hard rock through the application of GIS. Environ Geol 55(3):467–475CrossRefGoogle Scholar
  71. Rahmati O, Nazari Samani A, Mahdavi M, Pourghasemi HR, Zeiniv H (2014a) Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS. Arab J Geosci. doi: 10.1007/s12517-014-1668-4 Google Scholar
  72. Rahmati O, Nazari Samani A, Mahmoodi N, Mahdavi M (2014b) Assessment of the contribution of N-fertilizers to nitrate pollution of groundwater in western Iran (Case Study: Ghorveh–Dehgelan Aquifer). Water Qual Expo Health (Lond). doi: 10.1007/s12403-014-0135-5 Google Scholar
  73. Rao BV, Briz-Kishore BH (1991) A methodology for locating potential aquifers in a typical semi-arid region in India using resistivity and hydrogeologic parameters. Geoexploration 27:55–64CrossRefGoogle Scholar
  74. Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2013) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in central Nepal Himalaya. Arab J Geosci. doi: 10.1007/s12517-012-0807-z Google Scholar
  75. Saaty TL (1980) The analytic hierarchy process: planning, priority setting. Resource Allocation, McGraw-Hill, New YorkGoogle Scholar
  76. Sander P, Chesley MM, Minor TB (1996) Groundwater assessment using remote sensing and GIS in a rural groundwater project in Ghana: lessons learned. Hydrogeol J 4(3):40–49CrossRefGoogle Scholar
  77. Sarkar BC, Deota BS, Raju PLN, Jugran DK (2001) A geographic information system approach to evaluation of groundwater potentiality of Shamri micro-watershed in the Shimla Taluk, Himachal Pradesh. J Indian Soc Remote Sens 29(3):151–164CrossRefGoogle Scholar
  78. Shahid S, Nath SK, Kamal ASMM (2002) GIS integration of remote sensing and topographic data using fuzzy logic for ground water assessment in Midnapur District, India. Geocarto Int 17(3):69–74CrossRefGoogle Scholar
  79. 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. doi: 10.1080/10106049.2014.894584 Google Scholar
  80. Singh AK, Prakash SR (2003) An integrated approach of remote sensing, geophysics and GIS to evaluation of groundwater potentiality of Ojhala sub-watershed, Mirjapur District, U.P., India. http://www. Accessed 25 Aug 2007Google Scholar
  81. Solomon S, Quiel F (2006) Groundwater study using remote sensing and geographic information systems (GIS) in the central highlands of Eritrea. Hydrogeol J 14:1029–1041CrossRefGoogle Scholar
  82. Stafford DB (ed) (1991) Civil engineering applications of remote sensing and geographic information systems. ASCE, New YorkGoogle Scholar
  83. Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79CrossRefGoogle Scholar
  84. Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343CrossRefGoogle Scholar
  85. Todd DK, Mays LW (1980) Groundwater hydrology, 2nd edn. Wiley, New YorkGoogle Scholar
  86. Vaux H (2011) Groundwater under stress: the importance of management. Environ. Earth Sci 62:19–23CrossRefGoogle Scholar
  87. Vieux BE (2004) Distributed hydrologic modeling using GIS. Water Sci Tech Libr, vol. 48. Kluwer Academic Publishers, p 312Google Scholar
  88. Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibilitymapping in Turkey. Ph.D Thesis Department of Geomatics the University of Melbourne, p 423Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Centre for Geoinformatics and Planetary Studies, Department of GeologyPeriyar UniversitySalemIndia

Personalised recommendations