• Applications of Data Science Technologies in the Field of Groundwater Science and Future Trends
  • Jina Jeong1·Jae Min Lee2·Subi Lee1·Woojong Yang3·Weon Shik Han3*

  • 1Department of Geology, Kyungpook National University, Daegu 41566, Korea
    2Groundwater Environmental Research Center, Korea Institute of Geoscience and Mineral Resources, Daejeon 34132, Korea
    3Department of Earth System Sciences, Yonsei University, Seoul 03722, Korea

  • 데이터 사이언스 기술의 지하수 분야 응용 사례 분석 및 발전 방향
  • 정진아1·이재민2·이수비1·양우종3·한원식3*

  • 1경북대학교 지질학과, 2한국지질자원연구원 지하수환경연구센터, 3연세대학교 지구시스템과학과

  • 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.

Abstract

Rapid development of geophysical exploration and hydrogeologic monitoring techniques has yielded remarkable increase of datasets related to groundwater systems. Increased number of datasets contribute to understanding of general aquifer characteristics such as groundwater yield and flow, but understanding of complex heterogenous aquifers system is still a challenging task. Recently, applications of data science technique have become popular in the fields of geophysical explorations and monitoring, and such attempts are also extended in the groundwater field. This work reviewed current status and advancement in utilization of data science in groundwater field. The application of data science techniques facilitates effective and realistic analyses of aquifer system, and allows accurate prediction of aquifer system change in response to extreme climate events. Due to such benefits, data science techniques have become an effective tool to establish more sustainable groundwater management systems. It is expected that the techniques will further strengthen the theoretical framework in groundwater management to cope with upcoming challenges and limitations.


Keywords: Data science, Groundwater data analysis, Data acquisition, Data quality, Intelligent groundwater quantity and quality management system

This Article

  • 2023; 28(S1): 18-39

    Published on Jan 31, 2023

  • 10.7857/JSGE.2023.28.S.018
  • Received on Oct 20, 2022
  • Revised on Nov 3, 2022
  • Accepted on Nov 17, 2022

Correspondence to

  • Weon Shik Han
  • Department of Earth System Sciences, Yonsei University, Seoul 03722, Korea

  • E-mail: hanw@yonsei.ac.kr