Skip to main content

A Comprehensive Review on Mapping of Groundwater Potential Zones: Past, Present and Future Recommendations

  • Chapter
  • First Online:
Emerging Technologies for Water Supply, Conservation and Management

Part of the book series: Springer Water ((SPWA))

Abstract

The over exploitation of groundwater resources is a highly thought-provoking issue, which hinders the goal of sustainable water management worldwide. Hence, it is utmost necessary to identify the groundwater reserves in terms of potential areas/zones, average yield, and seasonal recharge. Within last decades a substantial progress has been observed in the delineation of Groundwater Potential Zones (GPZ) and have successfully applied bi-variate model, Multi-criteria Decision Making (MCDM) models, state of the art Machine Learning (ML) model, Ensemble model and metaheuristics models in the development of GPZ. However, still a research gap exists in the demarcation of GPZ both in terms of groundwater potential model development and groundwater conditioning factor selection which is very significant from the scientific and policy maker’s point of view. Thus, the present review article aspires to render a more vivid understanding of future aspects of groundwater potential model development and the milestone achieved in the past. This review article covers all types of models applied in the demarcation of GPZs, selection of different groundwater conditioning factors, and type of data used (remote sensing and ground truth). The present article also comes up with all possible criteria and statistical methods for the evaluation of the model’s performance and accuracy. Furthermore, recommendation for potential future research direction to enhance the model prediction accuracy is also outlined in the present article which will be highly effective for the groundwater agencies and organisation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abijith D, Saravanan S, Singh L, Jennifer JJ, Saranya T, Parthasarathy KSS (2020) GIS-based multi-criteria analysis for identification of potential groundwater recharge zones—A case study from Ponnaniyaru watershed, Tamil Nadu, India. HydroResearch 3:1–14

    Google Scholar 

  2. Agarwal E, Agarwal R, Garg RD, Garg PK (2013) Delineation of groundwater potential zone: an AHP/ANP approach. J Earth Syst Sci 122(3):887–898

    Article  Google Scholar 

  3. Al-Abadi AM, Al-Temmeme AA, Al-Ghanimy MA (2016) A GIS-based combining of frequency ratio and index of entropy approaches for mapping groundwater availability zones at Badra–Al Al-Gharbi–Teeb areas, Iraq. Sustain Water Resour Manag 2(3):265–283

    Article  Google Scholar 

  4. Al-Hurban A, Al-Ruwaih F, Al-Dughairi A (2021) Quantitative geomorphological and hydromorphometric analysis of drainage Basins of as Sabriyah (Kuwait) using GIS techniques. J Geogr Inf Syst 13(02):166–193

    Google Scholar 

  5. Allafta H, Opp C, Patra S (2021) Identification of groundwater potential zones using remote sensing and GIS techniques: a case study of the Shatt Al-Arab Basin. Remote Sens 13(1):112 (Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  6. Amiri V, Rezaei M, Sohrabi N (2014) Groundwater quality assessment using entropy weighted water quality index (EWQI) in Lenjanat, Iran. Environ Earth Sci 72(9):3479–3490

    Article  Google Scholar 

  7. An P, Moon WM, Bonham-Carter GF (1994) Uncertainty management in integration of exploration data using the belief function. Nonrenew Resour 3(1):60–71

    Article  Google Scholar 

  8. Arabameri A, Rezaei K, Cerda A, Lombardo L, Rodrigo-Comino J (2019) GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches. Sci Total Environ 658:160–177

    Article  Google Scholar 

  9. Bagyaraj M, Ramkumar T, Venkatramanan S, Gurugnanam B (2013) Application of remote sensing and GIS analysis for identifying groundwater potential zone in parts of Kodaikanal Taluk, South India. Front Earth Sci 7(1):65–75

    Article  Google Scholar 

  10. Bandara A, Hettiarachchi Y Hettiarachchi K, Munasinghe S, Wijesinghe I Thayasivam U (2020) A generalized ensemble machine learning approach for landslide susceptibility modelling. In: Sharma N, Chakrabarti A, Balas VE (eds) Data management, analytics and innovation. Advances in intelligent systems and computing, vol 1016. Springer Singapore, Singapore, pp 71–93. http://link.springer.com/https://doi.org/10.1007/978-981-13-9364-8_6

  11. Barpi F (2004) Fuzzy modelling of powder snow avalanches. Cold Reg Sci Technol 40(3):213–227

    Article  Google Scholar 

  12. Berhanu B, Seleshi Y, Melesse AM (2014) Surface water and groundwater resources of ethiopia: potentials and challenges of water resources development. In: Melesse AM, Abtew W, Setegn SG (eds) Nile River Basin: ecohydrological challenges, climate change and hydropolitics. Springer International Publishing, Cham, pp 97–117. https://doi.org/10.1007/978-3-319-02720-3_6

  13. Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Comput Methods Geosci 13:398

    Google Scholar 

  14. Boori M, Choudhary K, Kupriyanov A (2019) Identification and mapping of groundwater potential zone through remote sensing and GIS technology in Kalmykia, Russia. Int J Geoinform 15(1)

    Google Scholar 

  15. Braham M, Boufekane A, Bourenane H, Nait Amara B, Bensalem R, Oubaiche EH, Bouhadad Y (2022) Identification of groundwater potential zones using remote sensing, GIS, machine learning and electrical resistivity tomography techniques in Guelma basin, northeastern Algeria. Geocarto Int 0(ja):1–24 (Taylor & Francis)

    Google Scholar 

  16. CGWB (2012) Manual on aquifer mapping, Government of India, Ministry of water resources. Government of India, Ministry of Water Resources, Central ground Water Board, p 72

    Google Scholar 

  17. Carranza EJM, Hale M (2003) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22(1):117–132

    Article  Google Scholar 

  18. Chaminé HI, Carvalho JM, Teixeira J, Freitas L (2015) Role of hydrogeological mapping in groundwater practice: back to basics. Eur Geol J 40:34–42

    Google Scholar 

  19. Chaudhry AK, Kumar K, Alam MA (2021) Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique. Geocarto Int 36(20):2323–2344 (Taylor & Francis)

    Google Scholar 

  20. Chen W, Pradhan B, Li S, Shahabi H, Rizeei HM, Hou E, Wang S (2019) Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat Resour Res 28(4):1239–1258

    Article  Google Scholar 

  21. Chen H, Zhang W, Nie N, Guo Y (2019) Long-term groundwater storage variations estimated in the Songhua River Basin by using GRACE products, land surface models, and in-situ observations. In: Science of the total environment, vol 649. Elsevier B.V., pp 372–387

    Google Scholar 

  22. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference On Knowledge Discovery And Data Mining. ACM, San Francisco California USA, pp 785–794. https://dl.acm.org/doi/https://doi.org/10.1145/2939672.2939785

  23. Chen Y, Shi F, Kirby JT, Wu G, Liang B (2020) A computationally efficient subgrid model for coupled surface and groundwater flows. Coast Eng 157(December 2019):103665. (Elsevier B.V.)

    Google Scholar 

  24. Choudhary S, Pingale SM, Khare D (2022) Delineation of groundwater potential zones of upper Godavari sub-basin of India using bi-variate, MCDM and advanced machine learning algorithms. Geocarto Int 1–32

    Google Scholar 

  25. Das S (2019) Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl: Soc Environ 14:60–74

    Google Scholar 

  26. Das B, Pal SC (2020) Assessment of groundwater recharge and its potential zone identification in groundwater-stressed Goghat-I block of Hugli District, West Bengal, India. Environ Dev Sustain 22(6):5905–5923

    Article  Google Scholar 

  27. Dempster AP (2008) Upper and lower probabilities induced by a multivalued mapping. In: Yager RR, Liu L (eds) Classic works of the dempster-shafer theory of belief functions, studies in fuzziness and soft computing. Springer, Berlin, Heidelberg, pp 57–72. https://doi.org/10.1007/978-3-540-44792-4_3

  28. Diakoulaki D, Mavrotas G, Papayannakis L (1995) Determining objective weights in multiple criteria problems: the critic method. Comput Oper Res 22(7):763–770

    Article  Google Scholar 

  29. Dietterich TG (2000) Ensemble methods in machine learning. In: Multiple classifier systems, Lecture notes in computer science. Springer, Berlin, Heidelberg, pp 1–15

    Google Scholar 

  30. Díaz-Alcaide S, Martínez-Santos P (2019) Review: advances in groundwater potential mapping. Hydrogeol J 27(7):2307–2324

    Article  Google Scholar 

  31. Elvis BWW, Arsène M, Théophile NM, Bruno KME, Olivier OA (2022) Integration of shannon entropy (SE), frequency ratio (FR) and analytical hierarchy process (AHP) in GIS for suitable groundwater potential zones targeting in the Yoyo river basin, Méiganga area, Adamawa Cameroon. J Hydrol: Region Stud 39:100997

    Google Scholar 

  32. Farzin M, Avand M, Ahmadzadeh H, Zelenakova M, Tiefenbacher JP (2021) Assessment of ensemble models for groundwater potential modeling and prediction in a Karst watershed. Water 13(18):2540 (Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  33. Forman EH (1993) Facts and fictions about the analytic hierarchy process. Math Comput Model 17(4):19–26

    Article  Google Scholar 

  34. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5). https://projecteuclid.org/journals/annals-of-statistics/volume-29/issue-5/Greedy-function-approximation-A-gradient-boosting-machine/https://doi.org/10.1214/aos/1013203451.full

  35. 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 Geoinf 13(1):127–139

    Google Scholar 

  36. Ghinoi A, Chung C-J (2005) STARTER: a statistical GIS-based model for the prediction of snow avalanche susceptibility using terrain features—Application to Alta Val Badia, Italian Dolomites. Geomorphology 66(1–4):305–325

    Article  Google Scholar 

  37. Ghosh PK, Bandyopadhyay S, Jana NC (2015) Mapping of groundwater potential zones in hard rock terrain using geoinformatics: a case of Kumari watershed in western part of West Bengal. Model Earth Syst Environ 2(1):1

    Article  Google Scholar 

  38. Gueretz JS, Da Silva FA, Simionatto EL, Férard JF, Radetski CM, Somensi CA (2019) A multi-parametric study of the interaction between the Parati river and Babitonga Bay in terms of water quality. J Environ Sci Health, Part B:1–8

    Google Scholar 

  39. Hajkowicz SA, McDonald GT, Smith PN (2000) An evaluation of multiple objective decision support weighting techniques in natural resource management. J Environ Plan Manag 43(4):505–518 (Routledge)

    Google Scholar 

  40. Hasanuzzaman M, Mandal MH, Hasnine M, Shit PK (2022) Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India. Appl Water Sci 12(4):58

    Article  Google Scholar 

  41. Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849–872

    Article  Google Scholar 

  42. Hou E, Wang J, Chen W (2018) A comparative study on groundwater spring potential analysis based on statistical index, index of entropy and certainty factors models. Geocarto Int 33(7):754–769

    Article  Google Scholar 

  43. Jaafarzadeh MS, Tahmasebipour N, Haghizadeh A, Pourghasemi HR, Rouhani H (2021) Groundwater recharge potential zonation using an ensemble of machine learning and bivariate statistical models. Sci Rep 11(1):5587 (Nature Publishing Group)

    Google Scholar 

  44. Jaccard C (1990) Fuzzy factorial analysis of snow avalanches. Nat Hazards 3(4):329–340

    Article  Google Scholar 

  45. Kanimozhi U, Ganapathy S, Manjula D, Kannan A (2019) An intelligent risk prediction system for breast cancer using fuzzy temporal rules. Natl Acad Sci Lett 42(3):227–232

    Article  Google Scholar 

  46. Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439

    Article  Google Scholar 

  47. Khosravi K, Panahi M, Tien Bui D (2018) Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization. Hydrol Earth Syst Sci 22(9):4771–4792 (Copernicus GmbH)

    Google Scholar 

  48. Kim G, Park CS, Yoon KP (1997) Identifying investment opportunities for advanced manufacturing systems with comparative-integrated performance measurement. Int J Prod Econ 50(1):23–33

    Article  Google Scholar 

  49. Kisi O, Alizamir M, Zounemat-Kermani M (2017) Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Nat Hazards 87(1):367–381

    Article  Google Scholar 

  50. Kumar M, Singh SK, Kundu A, Tyagi K, Menon J, Frederick A, Raj A, Lal D (2022) GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis. Appl Water Sci 12(4):71

    Article  Google Scholar 

  51. Kumar A, Krishna AP (2018) Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach. Geocarto Int 33(2):105–129 (Taylor & Francis)

    Google Scholar 

  52. Lee S, Kim Y-S, Oh H-J (2012) Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping. J Environ Manage 96(1):91–105

    Article  Google Scholar 

  53. Lee S, Hyun Y, Lee S, Lee MJ (2020) Groundwater potential mapping using remote sensing and gis-based machine learning techniques. Remote Sens 12(7):1200 (Multidisciplinary Digital Publishing Institute),

    Google Scholar 

  54. Li L, Lan H, Guo C, Zhang Y, Li Q, Wu Y (2017) A modified frequency ratio method for landslide susceptibility assessment. Landslides 14(2):727–741

    Article  Google Scholar 

  55. Li S, Li Y, Liu Z (2013) Hebei rural groundwater contamination and integrated control. J. Cangzhou Normal Univ 29(4):8–10

    Google Scholar 

  56. Liu H, Lang B (2019) Machine learning and deep learning methods for intrusion detection systems: A survey. Appl Sci 9(20):4396. https://doi.org/10.3390/app9204396

  57. Mahato S, Pal S (2019) Groundwater potential mapping in a rural River Basin by union (OR) and intersection (AND) of four multi-criteria decision-making models. Nat Resour Res 28(2):523–545

    Article  Google Scholar 

  58. Maity B, Mallick SK, Das P, Rudra S (2022) Comparative analysis of groundwater potentiality zone using fuzzy AHP, frequency ratio and Bayesian weights of evidence methods. Appl Water Sci 12(4):63

    Article  Google Scholar 

  59. Malik A, Tikhamarine Y, Al-Ansari N, Shahid S, Sekhon HS, Pal RK, Rai P, Pandey K, Singh P, Elbeltagi A, Sammen SS (2021) Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Eng Appl Comput Fluid Mech 15(1):1075–1094 (Taylor & Francis)

    Google Scholar 

  60. Masroor M, Rehman S, Sajjad H, Rahaman MH, Sahana M, Ahmed R, Singh R (2021) Assessing the impact of drought conditions on groundwater potential in Godavari Middle Sub-Basin, India using analytical hierarchy process and random forest machine learning algorithm. Groundwater Sustain Dev 13:100554

    Google Scholar 

  61. Massey DS, Denton NA (1988) The dimensions of residential segregation. Soc Forces 67(2):281–315 (Oxford University Press)

    Google Scholar 

  62. Mirchooli F, Motevalli A, Pourghasemi HR, Mohammadi M, Bhattacharya P, Maghsood FF, Tiefenbacher JP (2019) How do data-mining models consider arsenic contamination in sediments and variables importance? Environ Monit Assess 191(12):777

    Article  Google Scholar 

  63. Mogaji KA, Omosuyi GO, Adelusi AO, Lim HS (2016) Application of GIS-based evidential belief function model to regional groundwater recharge potential zones mapping in Hardrock geologic terrain. Environ Process 3(1):93–123

    Article  Google Scholar 

  64. Moghaddam DD, Rezaei M, Pourghasemi HR, Pourtaghie ZS, Pradhan B (2015) Groundwater spring potential mapping using bivariate statistical model and GIS in the Taleghan Watershed, Iran. Arab J Geosci 8(2):913–929

    Article  Google Scholar 

  65. Mohammady M, Pourghasemi HR, Pradhan B (2012) Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci 61:221–236 (Geological Anatomy of East and South Asia)

    Google Scholar 

  66. Mollinedo J, Schumacher TE, Chintala R (2015) Influence of feedstocks and pyrolysis on biochar’s capacity to modify soil water retention characteristics. J Anal Appl Pyrol 114:100–108

    Article  Google Scholar 

  67. Muavhi N, Thamaga KH, Mutoti MI (2021) Mapping groundwater potential zones using relative frequency ratio, analytic hierarchy process and their hybrid models: case of Nzhelele-Makhado area in South Africa. Geocarto Int 0(0):1–20 (Taylor & Francis)

    Google Scholar 

  68. 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(9):2643–2672

    Article  Google Scholar 

  69. Mumtaz R, Baig S, Kazmi SSA, Ahmad F, Fatima I, Ghauri B (2019) Delineation of groundwater prospective resources by exploiting geo-spatial decision-making techniques for the Kingdom of Saudi Arabia. Neural Comput Appl 31(9):5379–5399

    Article  Google Scholar 

  70. Murmu P, Kumar M, Lal D, Sonker I, Singh SK (2019) Delineation of groundwater potential zones using geospatial techniques and analytical hierarchy process in Dumka district, Jharkhand, India. Groundw Sustain Dev 9:100239

    Google Scholar 

  71. Naghibi SA, Hashemi H, Berndtsson R, Lee S (2020) Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors. J Hydrol 589:125197

    Article  Google Scholar 

  72. Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29(14):5217–5236

    Article  Google Scholar 

  73. Namous M, Hssaisoune M, Pradhan B, Lee C-W, Alamri A, Elaloui A, Edahbi M, Krimissa S, Eloudi H, Ouayah M, Elhimer H, Tagma T (2021) Spatial prediction of groundwater potentiality in large semi-arid and karstic mountainous region using machine learning models. Water 13(16):2273 (Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  74. Nampak H, Pradhan B, Manap MA (2014) Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. J Hydrol 513:283–300

    Article  Google Scholar 

  75. Nasir MJ, Khan S, Zahid H, Khan A (2018) Delineation of groundwater potential zones using GIS and multi influence factor (MIF) techniques: a study of district Swat, Khyber Pakhtunkhwa, Pakistan. Environ Earth Sci 77(10):367

    Article  Google Scholar 

  76. Nguyen HD (2022) Hybrid models based on deep learning neural network and optimization algorithms for the spatial prediction of tropical forest fire susceptibility in Nghe An province, Vietnam. Geocarto Int 0(0):1–25 (Taylor & Francis)

    Google Scholar 

  77. Oh H-J, Kim Y-S, Choi J-K, Park E, Lee S (2011) GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. J Hydrol 399(3):158–172

    Article  Google Scholar 

  78. Oikonomidis D, Dimogianni S, Kazakis N, Voudouris K (2015) A GIS/Remote Sensing-based methodology for groundwater potentiality assessment in Tirnavos area, Greece. J Hydrol 525:197–208

    Google Scholar 

  79. Ozdemir A (2011) 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):290–308

    Article  Google Scholar 

  80. Pal S, Kundu S, Mahato S (2020) Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh. J Clean Prod 257:120311 (Elsevier Ltd.)

    Google Scholar 

  81. Palacios AM, Palacios JL, Sánchez L, Alcalá-Fdez J (2015) Genetic learning of the membership functions for mining fuzzy association rules from low quality data. Inf Sci 295:358–378

    Article  Google Scholar 

  82. Park I, Kim Y, Lee S (2014) Groundwater productivity potential mapping using evidential belief function. Groundwater 52(S1):201–207

    Article  Google Scholar 

  83. Park S, Hamm S-Y, Jeon H-T, Kim J (2017) Evaluation of logistic regression and multivariate adaptive regression spline models for groundwater potential mapping using R and GIS. Sustainability 9(7):1157 (Multidisciplinary Digital Publishing Institute)

    Google Scholar 

  84. Paryani S, Neshat A, Pourghasemi HR, Ntona MM, Kazakis N (2022) A novel hybrid of support vector regression and metaheuristic algorithms for groundwater spring potential mapping. Sci Total Environ 807:151055

    Article  Google Scholar 

  85. Patra S, Sahoo S, Mishra P, Mahapatra SC (2018) Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. J Urban Manag 7(2):70–84 (Elsevier B.V.)

    Google Scholar 

  86. Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR (2013) Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci 6(7):2351–2365

    Article  Google Scholar 

  87. Pourghasemi HR, Beheshtirad M (2015) Assessment of a data-driven evidential belief function model and GIS for groundwater potential mapping in the Koohrang Watershed, Iran. Geocarto Int 30(6):662–685 (Taylor & Francis)

    Google Scholar 

  88. Pradhan S, Kumar S, Kumar Y, Sharma HC (2019) Assessment of groundwater utilization status and prediction of water table depth using different heuristic models in an Indian interbasin. Soft Comput 23(20):10261–10285

    Article  Google Scholar 

  89. Rahmati O, Moghaddam DD, Moosavi V, Kalantari Z, Samadi M, Lee S, Bui DT (2019) An automated Python language-based tool for creating absence samples in groundwater potential mapping. Remote Sens 11(11):1–22

    Article  Google Scholar 

  90. Rane NL, Jayaraj GK (2022) Comparison of multi-influence factor, weight of evidence and frequency ratio techniques to evaluate groundwater potential zones of basaltic aquifer systems. Environ Dev Sustain 24(2):2315–2344

    Article  Google Scholar 

  91. Razandi Y, Pourghasemi HR, Neisani NS, Rahmati O (2015) Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS. Earth Sci Inf 8(4):867–883

    Article  Google Scholar 

  92. Razavi-Termeh SV, Khosravi K, Sadeghi-Niaraki A, Choi S-M, Singh VP (2020) Improving groundwater potential mapping using metaheuristic approaches. Hydrol Sci J 65(16):2729–2749 (Taylor & Francis)

    Google Scholar 

  93. Rizeei HM, Pradhan B, Saharkhiz MA, Lee S (2019) Groundwater aquifer potential modeling using an ensemble multi-adoptive boosting logistic regression technique. J Hydrol 579:124172

    Article  Google Scholar 

  94. Saaty RW (1987) The analytic hierarchy process—What it is and how it is used. Math Modell 9(3):161–176

    Article  Google Scholar 

  95. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Euro J Oper Res 48(1):9–26 (Desicion making by the analytic hierarchy process: Theory and applications)

    Google Scholar 

  96. Sagi O, Rokach L (2020) Explainable decision forest: transforming a decision forest into an interpretable tree. Inf Fusion 61:124–138

    Article  Google Scholar 

  97. Shalev-Shwartz S, Ben-David S (2014) Understanding machine learning: from theory to algorithms, 1st edn. Cambridge University Press. https://www.cambridge.org/core/product/identifier/9781107298019/type/book

  98. Smola AJ, Schölkopf B (2004) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  Google Scholar 

  99. Tahmassebipoor N, Rahmati O, Noormohamadi F, Lee S (2015) Spatial analysis of groundwater potential using weights-of-evidence and evidential belief function models and remote sensing. Arab J Geosci 9(1):79

    Article  Google Scholar 

  100. Talukdar S, Mallick J, Sarkar SK, Roy SK, Islam ARMdT, Praveen B, Naikoo MW, Rahman A, Sobnam M (2022) Novel hybrid models to enhance the efficiency of groundwater potentiality model. Appl Water Sci 12(4):62

    Article  Google Scholar 

  101. Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Tot Environ 615:438–451

    Google Scholar 

  102. Thapa R, Gupta S, Gupta A, Reddy DV, Kaur H (2018) Use of geospatial technology for delineating groundwater potential zones with an emphasis on water-table analysis in Dwarka River basin, Birbhum, India. Hydrogeol J 26(3):899–922

    Article  Google Scholar 

  103. Tien Bui D, Tuan TA, Klempe H, Pradhan B, Revhaug I (2016) Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2):361–378

    Article  Google Scholar 

  104. Tolche AD (2021) Groundwater potential mapping using geospatial techniques: a case study of Dhungeta-Ramis sub-basin, Ethiopia. Geol Ecol Landsc 5(1):65–80 (Taylor & Francis)

    Google Scholar 

  105. Vasin S, Carle A, Lang U, Kirchholtes HJ (2016) A groundwater management plan for Stuttgart. Sci Total Environ 563–564:704–712

    Article  Google Scholar 

  106. Xu Y, Liu L, Zhang X (2019) Multilattices on typical hesitant fuzzy sets. Inf Sci 491:63–73

    Article  Google Scholar 

  107. Yariyan P, Avand M, Omidvar E, Pham QB, Linh NTT, Tiefenbacher JP (2021) Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto Int 1–35

    Google Scholar 

  108. Zehtabiyan-Rezaie N, Alvandifar N, Saffaraval F, Makkiabadi M, Rahmati N, Saffar-Avval M (2019) A solar-powered solution for water shortage problem in arid and semi-arid regions in coastal countries. Sustain Energy Technol Assess 35:1–11

    Google Scholar 

  109. Zischg A, Fuchs S, Keiler M, Meißl G (2005) Modelling the system behaviour of wet snow avalanches using an expert system approach for risk management on high alpine traffic roads. Nat Hazards Earth Syst Sci 5(6):821–832. (Copernicus GmbH)

    Google Scholar 

  110. Zolekar RB, Bhagat VS (2015) Multi-criteria land suitability analysis for agriculture in hilly zone: remote sensing and GIS approach. Comput Electron Agric 118:300–321

    Article  Google Scholar 

Download references

Acknowledgements

I would like to express my sincere gratitude to all those who have contributed to the development of this book chapter. First and foremost, I am thankful to the editor of this book, for giving me the opportunity to contribute to this important publication. I am also grateful to my supervisor, who provided invaluable guidance and support throughout the writing process. Their feedback and encouragement helped me refine my ideas and improve the quality of my work. I would also like to acknowledge the support of my colleagues, whose insightful discussions and constructive criticism helped shape my thinking on this topic.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sourav Choudhary .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Choudhary, S., Jain, J., Pingale, S.M., Khare, D. (2023). A Comprehensive Review on Mapping of Groundwater Potential Zones: Past, Present and Future Recommendations. In: Balaji, E., Veeraswamy, G., Mannala, P., Madhav, S. (eds) Emerging Technologies for Water Supply, Conservation and Management. Springer Water. Springer, Cham. https://doi.org/10.1007/978-3-031-35279-9_6

Download citation

Publish with us

Policies and ethics