World's Best Scientists 2026 revealed!
Kyung Hwa Cho

Kyung Hwa Cho

D-Index & Metrics

Engineering and Technology

D-Index
51
Citations
9019
World Ranking
3903
National Ranking
89

Kyung Hwa Cho publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Kyung Hwa Cho sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 169 publications — 35th percentile

35% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Kyung Hwa Cho D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Kyung Hwa Cho sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 51 D-Index — 62nd percentile

62% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Kyung Hwa Cho is affiliated with Korea University in South Korea and specializes in research primarily within the fields of Environmental Science and Engineering. Their scholarly output includes significant contributions to subfields such as Water Science and Technology, Environmental Engineering, Biomedical Engineering, Oceanography, and Electrical and Electronic Engineering.

Cho's research topics focus on applications of artificial intelligence and advanced technologies in environmental contexts. Key areas include Hydrological Forecasting Using AI, Membrane Separation Technologies, Water Quality Monitoring Technologies, Membrane-based Ion Separation Techniques, Marine and Coastal Ecosystems, Water Quality Monitoring and Analysis, and Hydrology and Watershed Management Studies.

Some of the notable recent papers authored or co-authored by Cho include:

  • Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil (2020, The Science of The Total Environment)
  • Machine-learning-based prediction and optimization of emerging contaminants' adsorption capacity on biochar materials (2023, Chemical Engineering Journal)
  • Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green (2022, Journal of Hazardous Materials)
  • A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds (2020, Journal of Hydrology)
  • Using convolutional neural network for predicting cyanobacteria concentrations in river water (2020, Water Research)

The venues where Cho frequently publishes reflect a focus on environmental and water-related research. These include:

  • Water Research
  • Desalination
  • The Science of The Total Environment
  • Journal of Hazardous Materials
  • Journal of Cleaner Production

Collaborative work is a significant aspect of Cho's career, with frequent coauthors including Sang-Soo Baek, JongCheol Pyo, Moon Son, Ather Abbas, and Jaegyu Shim.

In addition to articles, Cho has contributed to academic books, including a publication titled Deep Learning for Hydrometeorology and Environmental Science released by Springer Nature (Netherlands) in 2021.

Best Publications

  • Record-setting algal bloom in Lake Erie caused by agricultural and meteorological trends consistent with expected future conditions

    Anna M Michalak;Eric J Anderson;Dimitry Beletsky;Steven Boland

  • Prediction of effluent concentration in a wastewater treatment plant using machine learning models

    Hong Guo;Kwanho Jeong;Jiyeon Lim;Jeongwon Jo

  • Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea

    Yongeun Park;Kyung Hwa Cho;Jihwan Park;Sung Min Cha

  • Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approach.

    Sang-Soo Baek;Dong-Ho Choi;Jae-Woon Jung;Hyung-Jin Lee

  • Linking land-use type and stream water quality using spatial data of fecal indicator bacteria and heavy metals in the Yeongsan river basin.

    Joo-Hyon Kang;Seung Won Lee;Kyung Hwa Cho;Seo Jin Ki

  • Evaluating Causes of Trends in Long-Term Dissolved Reactive Phosphorus Loads to Lake Erie

    Irem Daloğlu;Kyung Hwa Cho;Donald Scavia

  • Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)

    Sechan Park;Minjeong Kim;Minhae Kim;Hyeong-Gyu Namgung

  • Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network.

    Kyung Hwa Cho;Suthipong Sthiannopkao;Yakov A. Pachepsky;Kyoung-Woong Kim

  • Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil

    JongCheol Pyo;Seok Min Hong;Yong Sung Kwon;Moon Sung Kim

  • A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery

    JongCheol Pyo;Hongtao Duan;Sangsoo Baek;Moon Sung Kim

  • Modeling Fate and Transport of Fecally-derived Microorganisms at the Watershed Scale: State of the Science and Future Opportunities

    Kyung Hwa Cho;Yakov A. Pachepsky;David M. Oliver;Richard W. Muirhead

  • Release of Escherichia coli from the bottom sediment in a first-order creek: Experiment and reach-specific modeling

    Kyung Hwa Cho;Kyung Hwa Cho;Y.A. Pachepsky;Joon Ha Kim;A.K. Guber

  • A multivariate study for characterizing particulate matter (PM10, PM2.5, and PM1) in Seoul metropolitan subway stations, Korea

    Soon-Bark Kwon;Wootae Jeong;Duckshin Park;Ki-Tae Kim

  • Meteorological effects on the levels of fecal indicator bacteria in an urban stream: a modeling approach.

    Kyung Hwa Cho;Sung Min Cha;Joo-Hyon Kang;Seung Won Lee

  • Novel activation of peroxymonosulfate by biochar derived from rice husk toward oxidation of organic contaminants in wastewater

    Pham Thi Huong;Kim Jitae;T.M. Al Tahtamouni;Nguyen Le Minh Tri

  • A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds

    Sang-Soo Baek;Mayzonee Ligaray;Jongcheol Pyo;Jong-Pyo Park

  • Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method.

    Jin Hwi Kim;Jae-Ki Shin;Hankyu Lee;Dong Hoon Lee

  • The modified SWAT model for predicting fecal coliforms in the Wachusett Reservoir Watershed, USA.

    Kyung Hwa Cho;Yakov A. Pachepsky;Joon Ha Kim;Jung-Woo Kim

  • Using convolutional neural network for predicting cyanobacteria concentrations in river water

    JongCheol Pyo;Lan Joo Park;Yakov Pachepsky;Sang-Soo Baek

  • Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir

    Yong Sung Kwon;JongCheol Pyo;Yong-Hwan Kwon;Hongtao Duan

Frequent Co-Authors

Joon Ha Kim
Joon Ha Kim Gwangju Institute of Science and Technology
Yakov A. Pachepsky
Yakov A. Pachepsky Agricultural Research Service
Vijay P. Singh
Vijay P. Singh Texas A&M University
Kyoung-Woong Kim
Kyoung-Woong Kim Gwangju Institute of Science and Technology
Olivier Ribolzi
Olivier Ribolzi Institut de Recherche pour le Développement
In Seop Chang
In Seop Chang Gwangju Institute of Science and Technology
Hongtao Duan
Hongtao Duan Chinese Academy of Sciences
Jungho Im
Jungho Im Ulsan National Institute of Science and Technology
Hee-Mock Oh
Hee-Mock Oh Korea Research Institute of Bioscience and Biotechnology
Chang-Ha Lee
Chang-Ha Lee Seoul National University

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