World's Best Scientists 2026 revealed!

D-Index & Metrics

Earth Science

D-Index
40
Citations
8147
World Ranking
5710
National Ranking
5

Overview

Kang-Kun Lee is affiliated with Seoul National University in South Korea and has contributed extensively to the fields of Environmental Science and Earth and Planetary Sciences. Their research activity spans various subfields, including Environmental Engineering, Geochemistry and Petrology, Geophysics, Ocean Engineering, and Global and Planetary Change.

Their work has been published frequently in several notable venues, with multiple publications in The Science of The Total Environment and SSRN Electronic Journal. Other common publication venues include Journal of Hydrology, Journal of Environmental Management, and Journal of Hazardous Materials.

Lee's research focuses on several main topics, such as Groundwater flow and contamination studies, Groundwater and Isotope Geochemistry, CO2 Sequestration and Geologic Interactions, earthquake and tectonic studies, Earthquake Detection and Analysis, Radioactivity and Radon Measurements, and Seismology and Earthquake Studies.

Frequent coauthors of Lee include the following researchers:

  • Dugin Kaown
  • YeoJin Ju
  • Seong-Sun Lee
  • Jaeyeon Kim
  • Won-Tak Joun

Among recent publications, selected works include:

  • Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers, 2022, Applied Energy
  • Causal mechanism of injection-induced earthquakes through the Mw 5.5 Pohang earthquake case study, 2020, Nature Communications
  • Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy, 2020, Journal of Environmental Management
  • Application of machine learning to predict CO2 trapping performance in deep saline aquifers, 2021, Energy
  • Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping, 2022, Fuel

Their work often integrates machine learning and geospatial modeling techniques to address environmental challenges. Specific research includes predictions of CO2 sequestration potential, modeling of earthquake mechanisms related to injection activities, and analysis of groundwater contamination. This multidisciplinary approach intersects environmental science, geophysics, and engineering principles.

Best Publications

  • A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer

    Heesung Yoon;Seong-Chun Jun;Yunjung Hyun;Gwang-Ok Bae

  • Characterization of a Ground Water Hydrochemical System Through Multivariate Analysis: Clustering into Ground Water Zones

    Heejun Suk;Kang-Kun Lee

  • Does sea-level rise have an impact on saltwater intrusion?

    Sun Woo Chang;T. Prabhakar Clement;Matthew J. Simpson;Kang-Kun Lee

  • Use of hydrologic time series data for identification of recharge mechanism in a fractured bedrock aquifer system

    Jin-Yong Lee;Kang-Kun Lee

  • Managing injection-induced seismic risks.

    Kang-Kun Lee;William L. Ellsworth;Domenico Giardini;John Townend

  • Large submarine groundwater discharge (SGD) from a volcanic island

    Guebuem Kim;Kang-Kun Lee;Kwan-Suk Park;Dong-Woon Hwang

  • Predicting groundwater level fluctuations with meteorological effect implications-A comparative study among soft computing techniques

    Jalal Shiri;Ozgur Kisi;Heesung Yoon;Kang-Kun Lee

  • Statistical evaluation of geochemical parameter distribution in a ground water system contaminated with petroleum hydrocarbons.

    Jin-Yong Lee;Jeong-Yong Cheon;Kang-Kun Lee;Seok-Young Lee

  • Identification of nitrate and sulfate sources in groundwater using dual stable isotope approaches for an agricultural area with different land use (Chuncheon, mid-eastern Korea)

    Dugin Kaown;Dong-Chan Koh;Bernhard Mayer;Kang-Kun Lee

  • Knowledge-based machine learning techniques for accurate prediction of CO2 storage performance in underground saline aquifers

    Unknown

  • Nitrogen transformation and transport modeling in groundwater aquifers

    Mee-Sun Lee;Kang-Kun Lee;Yunjung Hyun;T. Prabhakar Clement

  • A method to improve the stability and accuracy of ANN- and SVM-based time series models for long-term groundwater level predictions

    Heesung Yoon;Yunjung Hyun;Kyoochul Ha;Kang-Kun Lee

  • Identifying the sources of nitrate contamination of groundwater in an agricultural area (Haean basin, Korea) using isotope and microbial community analyses

    Heejung Kim;Dugin Kaown;Bernhard Mayer;Jin-Yong Lee

  • Assessment of the validity of Stokes and Reynolds equations for fluid flow through a rough-walled fracture with flow imaging

    Seung Hyun Lee;Kang-Kun Lee;In Wook Yeo

  • Application of machine learning to predict CO2 trapping performance in deep saline aquifers

    Unknown

  • Using artificial neural network models for groundwater level forecasting and assessment of the relative impacts of influencing factors

    Sanghoon Lee;Kang-Kun Lee;Heesung Yoon

  • Application of robust intelligent schemes for accurate modelling interfacial tension of CO2 brine systems: Implications for structural CO2 trapping

    Unknown

  • Generalized solution to multi-dimensional multi-species transport equations coupled with a first-order reaction network involving distinct retardation factors

    Cristhian R. Quezada;T.Prabhakar Clement;Kang-Kun Lee

  • Transport and intersection mixing in random fracture networks with power law length distributions

    Young-Jin Park;Jean-Raynald de Dreuzy;Kang-Kun Lee;Brian Berkowitz

  • Application of geographically weighted regression models to predict spatial characteristics of nitrate contamination: Implications for an effective groundwater management strategy.

    Eun-Hee Koh;Eunhee Lee;Kang-Kun Lee

  • Factors affecting the spatial pattern of nitrate contamination in shallow groundwater.

    Dugin Kaown;Yunjung Hyun;Gwang-Ok Bae;Kang-Kun Lee

  • Urbanization and the groundwater budget, metropolitan Seoul area, Korea

    Yoon-Young Kim;Kang-Kun Lee;Ig Hwan Sung

  • Domenico Solution—Is It Valid?

    V. Srinivasan;T.P. Clement;K.K. Lee

  • Factors affecting the distribution of hydrocarbon contaminants and hydrogeochemical parameters in a shallow sand aquifer.

    Jin-Yong Lee;Jeong-Yong Cheon;Kang-Kun Lee;Seok-Young Lee

Frequent Co-Authors

Bernhard Mayer
Bernhard Mayer University of Calgary
Seong Taek Yun
Seong Taek Yun Korea University
Edward A. Sudicky
Edward A. Sudicky University of Waterloo
Brian Berkowitz
Brian Berkowitz Weizmann Institute of Science
Robert J. Glass
Robert J. Glass Sandia National Laboratories
Shemin Ge
Shemin Ge University of Colorado Boulder
Marco Petitta
Marco Petitta Sapienza University of Rome
Jin Yong Lee
Jin Yong Lee Sungkyunkwan University
David P. Hamilton
David P. Hamilton Griffith University
Kug Sun Hong
Kug Sun Hong Seoul National University

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