• A Groundwater Potential Map for the Nakdonggang River Basin
  • Soonyoung Yu·Jaehoon Jung·Jize Piao·Hee Sun Moon·Heejun Suk·Yongcheol Kim*·Dong-Chan Koh·Kyung-Seok Ko·Hyoung-Chan Kim·Sang-Ho Moon·Jehyun Shin·Byoung Ohan Shim·Hanna Choi·Kyoochul Ha

  • Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 34132, Korea

  • 낙동강권역의 지하수 산출 유망도 평가
  • 유순영·정재훈·박길택·문희선·석희준·김용철*·고동찬·고경석·김형찬

  • 한국지질자원연구원

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

References
  • 1. Abesser, C. and Lewis, M., 2015, A semi-quantitative technique for mapping potential aquifer productivity on the national scale: example of England and Wales (UK), Hydrogeol. J., 23(8), 1677-1694.
  •  
  • 2. Adiat, K.A.N., Nawawi, M.N.M., and 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., 440-441, 75-89.
  •  
  • 3. Agarwal, E., Agarwal, R., Garg, R.D., and Garg, P.K., 2013, Delineation of groundwater potential zone: An AHP/ANP approach, J. Earth Syst. Sci., 122, 887-898.
  •  
  • 4. Agarwal, R. and Garg, P.K., 2016, Remote sensing and GIS based groundwater potential & recharge zones mapping using multi-criteria decision making technique, Water Resour. Manag., 30, 243-260.
  •  
  • 5. Arulbalaji, P., Padmalal, D., and Sreelash, K., 2019, GIS and AHP techniques based delineation of groundwater potential zones: a case study from Southern Western Ghats, India, Sci. Rep., 9, 2082.
  •  
  • 6. Bae, S., Park, S., and Kim, S.J., 2015, The enhancement strategy on national cyber capability using hybrid methodology of AHP and TOPSIS, Converg. Secur. J., 15(4), 43-55.
  •  
  • 7. Çelik, R., 2019, Evaluation of groundwater potential by GIS-based multicriteria decision making as a spatial prediction tool: Case study in the tigris river batman-hasankeyf sub-basin, Turkey, Water, 11(12), 2630.
  •  
  • 8. Chaudhry, A.K., Kumar, K., and Alam, M.A., 2021, Mapping of groundwater potential zones using the fuzzy analytic hierarchy process and geospatial technique, Geocarto Int., 36(20), 2323-2344.
  •  
  • 9. Chung, E.-S., Won, K.J., Kim, Y.J., and Lee, H.S., 2014, Water resources vulnerability characteristics by district's population size in a changing climate using subjective and objective weights, Sustainability, 6(9), 6141-6157.
  •  
  • 10. Das, S., 2019, Comparison among influencing factor, frequency ratio, and analytical hierarchy process techniques for groundwater potential zonation in Vaitarna basin, Maharashtra, India, Groundw. Sustain. Dev., 8, 617-629.
  •  
  • 11. Díaz-Alcaide, S. and Martínez-Santos, P., 2019, Review: Advances in groundwater potential mapping, Hydrogeol. J., 27, 2307-2324.
  •  
  • 12. Geological Survey Ireland, 2023, Aquifer classification. Available online: https://www.gsi.ie/ (Accessed at December 15, 2023)
  •  
  • 13. Gómez-Escalonilla, V., Martínez-Santos, P., and Martín-Loeches, M., 2022, Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali, Hydrol. Earth Syst. Sci., 26(2), 221-243.
  •  
  • 14. Hasanuzzaman, M., Mandal, M.H., Hasnine, M., and Shit, P.K., 2022, Groundwater potential mapping using multi‑criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India, Appl. Water Sci., 12, 58.
  •  
  • 15. Hwang, C.L. and Yoon, K., 1981, Multiple Attributes Decision Making Methods and Applications. Springer, Heidelberg, Germany.
  •  
  • 16. Jenifer, M.A. and Jha, M.K., 2017, Comparison of analytic hierarchy process, catastrophe and entropy techniques for evaluating groundwater prospect of hard-rock aquifer systems, J. Hydrol., 548, 605-624.
  •  
  • 17. KIGAM (Korea Institute of Geoscience and Mineral Resources), 2019. 1:1,000,000 Digital geological map. Available online: https://data.kigam.re.kr/ (Accessed at December 15, 2023)
  •  
  • 18. KIGAM, 2021, Groundwater information map: the Geumgang River Basin. ISSN: 979-11-90505-22-2, 65p
  •  
  • 19. KIGAM, 2023, Groundwater information map of the Nakdonggang River Basin. ISSN: 979-11-90505-59-8, 76p
  •  
  • 20. KIGAM, 2022, Annual report of the basic research project of Korea Institute of Geoscience and Mineral resources: Development of Climate Change Adaptation Technologies for Securing and Utilizing Large-Scale Groundwater Resources, 238p
  •  
  • 21. Kim, Y.J. and Chung, E.-S., 2013, Assessing climate change vulnerability with group multi-criteria decision making approaches, Clim. Change, 121, 301-315.
  •  
  • 22. KMA (Korea Meteorological Administration), 2022, Automated Synoptic Observing System (ASOS) annual rainfall data. Available online: https://data.kma.go.kr (Accessed at December 15, 2023).
  •  
  • 23. KMOE (Ministry of Environment), 2019, Land cover map. Available online: https://egis.me.go.kr (Accessed at December 15, 2023).
  •  
  • 24. KOSTAT, 2019, Administrative divisions. Available online: http://data.nsdi.go.kr/ (Accessed at December 15, 2023).
  •  
  • 25. Kumar, T., Gautam, A.K., and Kumar, T., 2014, Appraising the accuracy of GIS-based Multi-criteria decision making technique for delineation of Groundwater potential zones, Water Resour. Manag., 28, 4449-4466.
  •  
  • 26. K-water, 2010. River order map. Available online: http://www.wamis.go.kr (Accessed at December 15, 2023)
  •  
  • 27. K-water, 2019, Depth to groundwater. Available online: http://data.nsdi.go.kr/ (Accessed at December 15, 2023)
  •  
  • 28. Lee, J.-Y., Raza, M., and Park, Y.-C., 2018, Current status and management for the sustainable groundwater resources in Korea, Episodes, 41(3), 179-191.
  •  
  • 29. Lee, S., Kim, Y.-S., and Oh, H.-J., 2012, Application of a weights-of-evidence method and GIS to regional groundwater productivity potential mapping, J. Environ. Manag., 96(1), 91-105
  •  
  • 30. .Lee, S. and Lee, C.-W., 2015, Application of decision-tree model to groundwater productivity-potential mapping, Sustainability, 7(10), 13416-13432.
  •  
  • 31. Lee, S., Hyun, Y., and Lee, M.-J., 2019, Groundwater potential mapping using data mining models of big data analysis in Goyang-si, South Korea, Sustainability, 11(6), 1678.
  •  
  • 32. Naghibi, S.A., Hashemi, H., Berndtsson, R., and 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.
  •  
  • 33. NIAS (National Institute of Agricultural Sciences), 1979, Drainage class map. Available online: http://soil.rda.go.kr (Accessed at December 15, 2023)
  •  
  • 34. NGII (National Geogrpahic Information Institute), 2021, Digital topographic map. Available online: http://data.nsdi.go.kr/ (Acc- essed at December 15, 2023)
  •  
  • 35. Patidar, N., Mohseni, U., Pathan, A. I., and Agnihotri, P.G., 2022. Groundwater potential zone mapping using an integrated approach of GIS‑Based AHP‑TOPSIS in Ujjain District, Madhya Pradesh, India. Water Conserv. Sci. En., 7, 267-282.
  •  
  • 36. Prasad, P., Loveson, V.J., Kotha, M., and Yadav, R., 2020, Application of machine learning techniques in groundwater potential mapping along the west coast of India, GISci. Remote Sens., 57, 735-752.
  •  
  • 37. Rahmati, O., Nazari Samani, A., Mahdavi, M., Pourghasemi, H.R., and Zeinivand, H., 2015, Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS, Arab. J. Geosci.,8, 7059-7071.
  •  
  • 38. Rasool, U., Yin, X., Xu, Z., Rasool, M. A. Senapathi, V., Hussain, M., Siddique, J., and Trabucco, J.C., 2022, Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan, Chemosphere, 303(Part 3), 135265.
  •  
  • 39. Ravenscroft, P. and Lytton, L., 2022, Seeing the Invisible: A Strategic Report on Groundwater Quality, World Bank, Washington, D.C.
  •  
  • 40. Razandi, Y., Pourghasemi, H.R., Neisani, N.S., and Rahmati, O., 2015, Application of analytical hierarchy process, frequency ratio, and certainty factor models for groundwater potential mapping using GIS, Earth Sci. Inform., 8, 867-883.
  •  
  • 41. Panahi, M.R., Mousavi, S.M., and Rahimzadegan, M., 2017, Delineation of groundwater potential zones using remote sensing, GIS, and AHP technique in Tehran-Karaj plain, Iran, Environ. Earth Sci., 76, 792.
  •  
  • 42. Saaty, R.W., 1980, The Analytic Hierarchy Process, Planning, Priority Setting, Resource Allocation. McGraw-Hill, New York.
  •  
  • 43. Shabani, M., Masoumi, Z., and Rezaei, A., 2022, Assessment of groundwater potential using multi-criteria decision analysis and geoelectrical surveying, Geo-spat. Inf. Sci., 25(4), 600-618.
  •  
  • 44. Taylor, R., Scanlon, B., Döll, P. et al., 2013, Ground water and climate change, Nature Clim. Change,3, 322-329.
  •  
  • 45. Tolche, A.D.D., 2021, Groundwater potential mapping using geospatial techniques: A case study of dhungeta-ramis sub-basin, Ethiopia, Geol. Ecol. Landsc., 5(1), 65-80.
  •  
  • 46. Trabelsi, F., Lee, S., Khlifi, S., and Arfaoui, A., 2019, Frequency ratio model for mapping groundwater potential zones using gis and remote sensing; Medjerda watershed Tunisia. In: Chaminé, H., Barbieri, M., Kisi, O., Chen, M., Merkel, B. (eds) Advances in Sustainable and Environmental Hydrology, Hydrogeology, Hydrochemistry and Water Resources, CAJG 2018. Advances in Science, Technology & Innovation, Springer, Cham.
  •  
  • 47. Won, K.J, Chung, E.-S, Kim, Y.J., and Hong, I.P., 2014, Assessment of water resources vulnerability index by nation, J. Korea Water Resour. Assoc., 47(2), 183-194.
  •  
  • 48. Yin, H., Shi, Y., Niu, H., Xie, D., Wei, J., Lefticariu, L., and Xu, S., 2018, A GIS-based model of potential groundwater yield zonation for a sandstone aquifer in the Juye Coalfield, Shangdong, China, J. Hydrol., 557, 434-447.
  •  

This Article

  • 2023; 28(6): 71-89

    Published on Dec 31, 2023

  • 10.7857/JSGE.2023.28.6.071
  • Received on Nov 30, 2023
  • Revised on Dec 10, 2023
  • Accepted on Dec 15, 2023

Correspondence to

  • Yongcheol Kim
  • Korea Institute of Geoscience and Mineral Resources (KIGAM), Daejeon 34132, Korea

  • E-mail: yckim@kigam.re.kr