GIS-Based Groundwater Potential Mapping in Khorramabad in Lorestan, Iran, using Frequency Ratio (FR) and Weights of Evidence (WoE) Models


In this study groundwater potential map of Khorramabad in Lorestan Province, Iran was produced using two different methods; Frequency Ratio (FR) and Weights of Evidence (WoE) within a geographic information system (GIS) environment. In the first step, wells location inventory map consisting of 212 wells with yield more than 10 m3/s was prepared, then 140 (70%) were randomly selected for training, while the remaining 72 (30%) were used for the model validation. Likewise, twelve influencing groundwater factors, namely altitude, slope angle, slope aspect, plan curvature, topographic wetness index, land use, drainage distance, drainage density, fault distance, fault density, geology and soil maps were prepared and integrated into spatial database. In order to analyze groundwater productivity, FR and WoE models were applied. The resulting maps were then classified into four categories namely low, moderate, high, and very high. In the last step, the produced maps were validated using receiver operating characteristic (ROC) technique. The areas under the ROC curve (AUC) was obtained 0.891 and 0.882 for the generated FR and WoE maps respectively, indicated the very good capability of both models for modeling groundwater potentiality in the study area.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 3.
Fig. 4.
Fig. 5.


  1. 1

    Aghdam, I.N., Pradhan, B., and Panahi, M., Landslide susceptibility assessment using a novel hybrid model of statistical bivariate methods (FR and WoE) and adaptive neuro-fuzzy inference system (ANFIS) at southern Zagros Mountains in Iran, Environ. Earth Sci., 2017, vol. 76, p. 237.

    Article  Google Scholar 

  2. 2

    Al-Abadi, A., 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., 2015.

  3. 3

    Al-Saud, M., Mapping potential areas for groundwater storage in Wadi Aurnah Basin, western Arabian Peninsula, using remote sensing and geographic information system techniques, Hydrogeol. J., 2010., vol. 24, pp. 1481–1495.

    Article  Google Scholar 

  4. 4

    Beven, K. and Kirkby, M., A physically based, variable contributing area model of basin hydrology/Un modèle base physique de zone d’appel variable de l’hydrologie du bassin versant, Hydrol. Sci. Bull., 1979, vol. 24, pp. 43–69.

    Article  Google Scholar 

  5. 5

    Bonahm-Carter, G.F., Agterberg, F.P., and Wright, D.F., Weights of evidence modeling: a new approach to mapping mineral potential, in Statistical Applications in the Earth Sciences: Geol Survey Canada, Agterbert, F.P., Bonham-Carter, G.F., Eds., 1989, pp. 171–183.

    Google Scholar 

  6. 6

    Bonham-Carter, G.F., Geographic Information Systems for Geoscientists: Modeling with GIS, Ontario, Canada: Pergamon, 1994.

    Google Scholar 

  7. 7

    Broxton, P.D., Troch, P.A., and Lyon, S.W., On the role of aspect to quantify water transit times in small mountainous catchments, Water Resour. Res., 2009, vol. 45.

  8. 8

    Carson, M.A. and Kirkby, M.J., Hillslope Form and Process, London: Cambridge University Press, 1972.

    Google Scholar 

  9. 9

    Corsini, A., Cervi, F., and Ronchetti, F., Weight of evidence and artificial neural networks for potential groundwater mapping: an application to the Mt. Modino area (Northern Apennines, Italy), Geomorph., 2009, vol. 111, pp. 79–87.

    Article  Google Scholar 

  10. 10

    Cui, K., Lu, D., and Li, W., Comparison of landslide susceptibility mapping based on statistical index, certainty factors, weights of evidence and evidential belief function models, Geocarto Int., 2016.

  11. 11

    El Mahdi, S.I. and Mohamed, M.M., Relationship between geological structures and groundwater flow and groundwater salinity in Al-Jaaw plain, United Arab Emirates, mapping and analysis by means of remote sensing and GIS, Arabian J. Geosci., 2014, vol. 7, pp. 1249–1259.

    Article  Google Scholar 

  12. 12

    El Mahdi, S.I. and Mohamed, M.M., Topographic attributes control groundwater flow and groundwater salinity of Al Ain, UAE: a prediction method using remote sensing and GIS, J. Environ. Earth Sci., 2012, vol. 2, pp. 1–13.

    Google Scholar 

  13. 13

    El Mahdy, S.I., Hydromorphological mapping and analysis for characterizing Darfur Paleolake, NW Sudan Using Remote Sensing and GIS, Int. J. Geosci., 2012, vol. 3, pp. 25–36.

    Article  Google Scholar 

  14. 14

    Ercanoglu, M. and Gokceoglu, C., Assessment of landslide susceptibility for a landslide prone area (north of Yenice, NW Turkey) by fuzzy approach, Environ. Geol., 2002, vol. 41, pp. 720–730.

    Article  Google Scholar 

  15. 15

    Gaur, S., Chahar, B.R., and Graillot, D., Combined use of groundwater modeling and potential zone analysis for management of groundwater, Int. J. Appl. Earth Obs., 2011, vol. 13, pp. 127–139.

    Article  Google Scholar 

  16. 16

    Gayen, A. and Saha, S., Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India, Model. Earth Syst. Environ., 2017, vol. 3, pp. 1123–1139.

    Article  Google Scholar 

  17. 17

    Hinckley, E.L.S., Ebel, B.A., Barnes, R.T., Anderson, R.S., Williams, M.W., and Anderson, S.P., Aspect control of water movement on hillslopes near the rain–snow transition of the Colorado Front Range, Hydrol. Process, 2014, vol. 28, pp. 74–85.

    Article  Google Scholar 

  18. 18

    Jha, M.K., Chowdary, V.M., and Chowdhury, A., Groundwater assessment in Salboni Block, West Bengal (India) using remote sensing, geographical information system and multi-criteria decision analysis techniques, Hydrogeol. J., 2010, vol. 18, pp. 1713–1728.

    Article  Google Scholar 

  19. 19

    Karami, G.H., Bagheri, R., and Rahimi, F., Determining the groundwater potential recharge zone and karst springs catchment area: Saldoran region, western Iran, Hydrogeol. J., 2016.

  20. 20

    Langston, A.L., Tucker, G.E., Anderson, R.S., and Anderson, S.P., Evidence for climatic and hillslope-aspect controls on vadose zone hydrology and implications for saprolite weathering, Earth Surf. Processes Landforms, 2015.

  21. 21

    Lee, S., Choi, J., and Min, K., Landslide susceptibility analysis and verification using the Bayesian probability model, Environ. Geol., 2002, vol. 43, pp. 120–131.

    Article  Google Scholar 

  22. 22

    Lee, S. and Pradhan, B., Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia, J. Earth Syst. Sci., 2006, vol. 115, pp. 661–672.

    Article  Google Scholar 

  23. 23

    Machiwal, D., Jha, M.K., and Mal, B.C., Assessment of groundwater potential in a semi-arid region of India using remote sensing, GIS and MCDM techniques, Water Resour. Manage., 2011, vol. 25, pp. 1359–1386.

    Article  Google Scholar 

  24. 24

    Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W.N.A., and Ramli, M.F., Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS, Arabian J. Geosci., 2014, vol. 7, pp. 711–724.

    Article  Google Scholar 

  25. 25

    Mancini, F., Ceppi, C., and Ritrovato, G., GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy, Nat. Hazards Earth Syst. Sci., 2010, vol. 10, pp. 1851–1864.

    Article  Google Scholar 

  26. 26

    Manikandan, J., Kiruthika, A.M., and Sureshbabu, S., Evaluation of groundwater potential zones in Krishnagiri District, Tamil Nadu using MIF Technique, Intern. J. Innov. Res. Sci., Eng. Technol., 2014, vol. 3, pp. 10524–10534.

    Google Scholar 

  27. 27

    Mathew, J., Jha, V.K., and Rawat, G.S., Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand, Curr. Sci. India, 2007, vol. 92, no. 5.

  28. 28

    Meijerink, A.M., Bannert, D., Batelaan, O., Lubczynski, M.W., and Pointet, T., Remote sensing applications to groundwater, IHP-VI, Series on Groundwater, 2007, no. 16, p. 30.

  29. 29

    Montgomery, D.R. and Buffington, J.M., Channel-reach morphology in mountain drainage basins, Geol. Soc. Am. Bull., 1997, vol. 109, pp. 596–611.

    Article  Google Scholar 

  30. 30

    Nampak, H., Pradhan, B., and Manap, M.A., Application of GIS based data driven evidential belief function model to predict groundwater potential zonation, J. Hydrol., 2014, vol. 513, pp. 283–300.

    Article  Google Scholar 

  31. 31

    Nefeslioglu, H.A., Duman, T.Y., and Durmaz, S., Landslide susceptibility mapping for a part of tectonic Kelkit Valley (Eastern Black Sea region of Turkey), Geomorphology, 2008., vol. 94, pp. 401–418.

    Article  Google Scholar 

  32. 32

    Oh, H.J., Kim, Y.S., Choi, J.K., Park, E., and Lee, S., GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea, J. Hydrol., 2011, vol. 399, pp. 158–172.

    Article  Google Scholar 

  33. 33

    Ozdemir, A. and Altural, T., A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey, J. Asian Earth Sci., 2013.

  34. 34

    Ozdemir, A., Using a binary logistic regression method and GIS for evaluating and mapping the groundwater spring potential in the Sultan Mountains (Aksehir, Turkey), J. Hydrol., 2011, vol. 405, pp. 123–136.

    Article  Google Scholar 

  35. 35

    Papadopoulou-Vrynioti, K., Bathrellos, G.D., Skilodimou, H.D., Kaviris, G., and Makropoulos, K., Karst collapse susceptibility mapping considering peak ground acceleration in a rapidly growing urban area, Eng. Geol., 2013, vol. 58, pp. 77–88.

    Article  Google Scholar 

  36. 36

    Park, S., Choi, C., Kim, B., and Kim, J., Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea, Environ. Earth Sci., 2013, vol. 68, pp. 1443–1464.

    Article  Google Scholar 

  37. 37

    Prasad, R.K., Mondal, N.C., Banerjee, P., Nandakumar, M.V., and Singh, V.S., Deciphering potential groundwater zone in hard rock through the application of GIS, Environ. Geol., 2008, vol. 55, pp. 467–475.

    Article  Google Scholar 

  38. 38

    Ravilious, K., Iran Sinking as Groundwater Resources Disappear for National Geographic News, 2008, September 22.

  39. 39

    Samy, I., Shattri, M., Bujang, B.K., and Ahmad, R.M., Application of terrain analysis to the mapping and spatial pattern analysis of subsurface geological fractures of Kuala Lumpur limestone bedrock, Malaysia, Int. J. Remote Sens., 2012, vol. 33, pp. 3176–3196.

    Article  Google Scholar 

  40. 40

    Schilirò, L, Montrasio, L., and Mugnozza, G.S., Prediction of shallow landslide occurrence: Validation of a physically-based approach through a real case study, Sci. Total Environ., 2016, vols. 569–570, pp. 134–144.

    Article  Google Scholar 

  41. 41

    Schmitt, E., Weights of evidence mineral prospectivity modeling with ArcGIS, EOSC, 448 Directed Studies, 2010.

  42. 42

    Shaban, A., Khawlie, M., and Abdallah, C., Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon, Hydrogeol. J., 2006, vol. 14, pp. 433–443.

    Article  Google Scholar 

  43. 43

    Shekhar, S. and Pandey, A.C., 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., 2015, vol. 30, pp. 402–421.

    Article  Google Scholar 

  44. 44

    Srikanth Vuppala, N.V., Study of ground water quality analysis in industrial zone of Visakhapatnam, J. Adv. Lab. Res. Biol., 2013, vol. 3, pp. 231–236.

    Google Scholar 

  45. 45

    Subba Rao, N., Groundwater potential index in a crystalline terrain using remote sensing data, Environ. Geol., 2006, vol. 50, pp. 1067–1076.

    Article  Google Scholar 

  46. 46

    Venkateswarana, S. and Ayyanduraib, R., Groundwater potential zoning in Upper Gadilam River Basin Tamil Nadu, Aquatic Procedia, 2015, vol. 4, pp. 1275–1282.

    Article  Google Scholar 

  47. 47

    Wilson, J.P. and Gallant, J.C., Terrain Analysis Principles and Applications, New York, NY, USA: Wiley, 2000.

    Google Scholar 

  48. 48

    Xu, C., Xu, X., Dai, F., Xiao, J., Tan, X., and Yuan, R., Landslide hazard mapping using GIS and weight of evidence model in Qingshui River watershed of 2008 Wenchuan earthquake struck region, J. Earth Sci., 2012, vol. 23, pp. 97–120.

    Article  Google Scholar 

  49. 49

    Yesilnacar, E.K., The application of computational intelligence to landslide susceptibility mapping in Turkey, PhD Thesis, Department of Geomatics the Univ. Melbourne, 2005, p. 423.

  50. 50

    Zeinivand, H. and Ghorbani Nejad, S., Application of GIS-based data-driven models for groundwater potential mapping in Kuhdasht region of Iran, Geocarto International, 2018, vol. 33, no. 6, pp. 651–666.

    Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Fatemeh Falah or Hossein Zeinivand.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Fatemeh Falah, Hossein Zeinivand GIS-Based Groundwater Potential Mapping in Khorramabad in Lorestan, Iran, using Frequency Ratio (FR) and Weights of Evidence (WoE) Models. Water Resour 46, 679–692 (2019).

Download citation


  • groundwater potential mapping
  • frequency ratio
  • weights-of-evidence
  • AUC plot
  • Khorramabad