World's Best Scientists 2026 revealed!

D-Index & Metrics

Earth Science

D-Index
40
Citations
8147
World Ranking
5711
National Ranking
5

Kang-Kun Lee publication distribution in Earth Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Earth Science in 2026. The highlighted bar marks where Kang-Kun Lee sits on this spectrum.

38–42 publications: 1 scientists 43–47 publications: 0 scientists 48–52 publications: 3 scientists 53–57 publications: 8 scientists 58–62 publications: 11 scientists 63–67 publications: 16 scientists 68–72 publications: 24 scientists 73–77 publications: 31 scientists 78–82 publications: 49 scientists 83–87 publications: 77 scientists 88–92 publications: 86 scientists 93–97 publications: 88 scientists 98–102 publications: 92 scientists 103–107 publications: 84 scientists 108–112 publications: 102 scientists 113–117 publications: 108 scientists 118–122 publications: 117 scientists 123–127 publications: 126 scientists 128–132 publications: 146 scientists 133–137 publications: 114 scientists 138–142 publications: 102 scientists 143–147 publications: 127 scientists 148–152 publications: 102 scientists 153–157 publications: 112 scientists 158–162 publications: 102 scientists 163–167 publications: 127 scientists 168–172 publications: 121 scientists 173–177 publications: 112 scientists 178–182 publications: 109 scientists 183–187 publications: 98 scientists 188–192 publications: 92 scientists 193–197 publications: 105 scientists 198–202 publications: 77 scientists 203–207 publications: 80 scientists 208–212 publications: 89 scientists 213–217 publications: 70 scientists 218–222 publications: 74 scientists 223–227 publications: 74 scientists 228–232 publications: 70 scientists 233–237 publications: 67 scientists 238–242 publications: 59 scientists 243–247 publications: 67 scientists 248–252 publications: 51 scientists 253–257 publications: 46 scientists 258–262 publications: 41 scientists 263–267 publications: 39 scientists 268–272 publications: 34 scientists 273–277 publications: 39 scientists 278–282 publications: 35 scientists 283–287 publications: 36 scientists 288–292 publications: 35 scientists 293–297 publications: 29 scientists 298–302 publications: 23 scientists 303–307 publications: 39 scientists 308–312 publications: 34 scientists 313–317 publications: 23 scientists 318–322 publications: 18 scientists 323–327 publications: 18 scientists 328–332 publications: 21 scientists 333–337 publications: 20 scientists 338–342 publications: 15 scientists 343–347 publications: 13 scientists 348–352 publications: 24 scientists 353–357 publications: 25 scientists 358–362 publications: 10 scientists 363–367 publications: 20 scientists 368–372 publications: 17 scientists 373–377 publications: 18 scientists 378–382 publications: 15 scientists 383–387 publications: 7 scientists 388–392 publications: 22 scientists 393–397 publications: 9 scientists 398–402 publications: 11 scientists 403–407 publications: 16 scientists 408–412 publications: 4 scientists 413–417 publications: 5 scientists 418–422 publications: 14 scientists 423–427 publications: 8 scientists 428–432 publications: 7 scientists 433–437 publications: 7 scientists 438–442 publications: 10 scientists 443–447 publications: 10 scientists 448–452 publications: 3 scientists 453–457 publications: 9 scientists 458–462 publications: 11 scientists 463–467 publications: 4 scientists 468–472 publications: 6 scientists 473–477 publications: 11 scientists 478–482 publications: 8 scientists 483–487 publications: 2 scientists 488–492 publications: 3 scientists 493–497 publications: 4 scientists 498–502 publications: 4 scientists 503–507 publications: 4 scientists 508–509 publications: 4 scientists 510+ publications: 100 scientists
38 publications 510+

This scientist: 267 publications — 80th percentile

80% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 510 publications or more.

Kang-Kun Lee D-index placement in Earth Science in 2026

The chart shows the D-index (discipline H-index) distribution of Earth Science scientists ranked by Research.com in 2026. The highlighted bar marks where Kang-Kun Lee sits on this spectrum.

30 D-Index: 15 scientists 31 D-Index: 42 scientists 32 D-Index: 60 scientists 33 D-Index: 90 scientists 34 D-Index: 103 scientists 35 D-Index: 108 scientists 36 D-Index: 142 scientists 37 D-Index: 147 scientists 38 D-Index: 147 scientists 39 D-Index: 152 scientists 40 D-Index: 143 scientists 41 D-Index: 157 scientists 42 D-Index: 141 scientists 43 D-Index: 151 scientists 44 D-Index: 140 scientists 45 D-Index: 135 scientists 46 D-Index: 121 scientists 47 D-Index: 108 scientists 48 D-Index: 128 scientists 49 D-Index: 111 scientists 50 D-Index: 106 scientists 51 D-Index: 112 scientists 52 D-Index: 102 scientists 53 D-Index: 103 scientists 54 D-Index: 94 scientists 55 D-Index: 61 scientists 56 D-Index: 101 scientists 57 D-Index: 71 scientists 58 D-Index: 65 scientists 59 D-Index: 78 scientists 60 D-Index: 93 scientists 61 D-Index: 56 scientists 62 D-Index: 58 scientists 63 D-Index: 52 scientists 64 D-Index: 63 scientists 65 D-Index: 49 scientists 66 D-Index: 53 scientists 67 D-Index: 57 scientists 68 D-Index: 50 scientists 69 D-Index: 44 scientists 70 D-Index: 44 scientists 71 D-Index: 31 scientists 72 D-Index: 33 scientists 73 D-Index: 33 scientists 74 D-Index: 23 scientists 75 D-Index: 32 scientists 76 D-Index: 24 scientists 77 D-Index: 17 scientists 78 D-Index: 19 scientists 79 D-Index: 9 scientists 80 D-Index: 21 scientists 81 D-Index: 20 scientists 82 D-Index: 17 scientists 83 D-Index: 19 scientists 84 D-Index: 19 scientists 85 D-Index: 14 scientists 86 D-Index: 6 scientists 87 D-Index: 14 scientists 88 D-Index: 17 scientists 89 D-Index: 10 scientists 90 D-Index: 8 scientists 91 D-Index: 8 scientists 92 D-Index: 9 scientists 93 D-Index: 6 scientists 94+ D-Index: 98 scientists
30 D-Index 94+

This scientist: 40 D-Index — 26th percentile

26% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 94 D-Index or more.

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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