Cho, Kyunghwa
Korea University · 環境科学
研究室紹介
Professor Kyunghwa Cho's research lab focuses on environmental microbiology and cancer signaling pathways, with a dual emphasis on watershed-scale modeling of microbial water quality and molecular mechanisms driving prostate cancer progression. The lab investigates fecal microorganism transport in natural waters using advanced watershed models to support environmental policy and water quality management. Simultaneously, it explores redox-dependent signaling cascades—particularly the ROS/STAT3/HIF-1α/TWIST1/N-cadherin axis—in prostate cancer, identifying novel biomarkers and therapeutic targets. This interdisciplinary approach bridges environmental health and molecular oncology.
microbial water qualitywatershed modelingprostate cancerROS signalingHIF-1α
Research Overview
Papers
323
Total Citations
10,222
Papers (5y)
124
Primary Field
環境科学
Research Output Trend
Figures are computed from collected data and may differ slightly.
Publications per year (5y)
124total
2022
2023
2024
2025
2026
Citations per year (5y)
2,473total
20222023202420252026
Selected Papers
151
Article|303 citations·2015
Prediction of effluent concentration in a wastewater treatment plant using machine learning modelsHong Guo, Kwanho Jeong, Jiyeon Lim, Jeongwon Jo, Young Mo Kim, Jong-pyo Park, Joon Ha Kim, Kyung Hwa Cho
SJR Q1Journal of Environmental SciencesOA
Environmental EngineeringEnvironmental Science
2
Article|251 citations·2015
Optimizing low impact development (LID) for stormwater runoff treatment in urban area, Korea: Experimental and modeling approachSang‐Soo Baek, Dongho Choi, Jae‐Woon Jung, Hyung‐Jin Lee, Hyuk Lee, Kwang‐Sik Yoon, Kyung Hwa Cho
SJR Q1Water ResearchOA
Environmental EngineeringEnvironmental Science
3
Article|190 citations·2023
Machine-learning-based prediction and optimization of emerging contaminants' adsorption capacity on biochar materialsZeeshan Haider Jaffari, Heewon Jeong, Jaegwan Shin, Jinwoo Kwak, Changgil Son, Yong-Gu Lee, Sang-Won Kim, Kangmin Chon, Kyung Hwa Cho
SJR Q1Chemical Engineering Journal
Water Science and TechnologyEnvironmental Science
4
Article|178 citations·2020
Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soilJongCheol Pyo, Seok Min Hong, Yong Sung Kwon, Moon S. Kim, Kyung Hwa Cho
SJR Q1The Science of The Total EnvironmentOA
Analytical ChemistryChemistry
5
Article|172 citations·2017
Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)Sechan Park, Minjeong Kim, Minhae Kim, Hyeong-Gyu Namgung, Ki‐Tae Kim, Kyung Hwa Cho, Soon-Bark Kwon
SJR Q1Journal of Hazardous Materials
Environmental EngineeringEnvironmental Science
6
Article|171 citations·2019
A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imageryJongCheol Pyo, Hongtao Duan, Sang‐Soo Baek, Moon S. Kim, Taegyun Jeon, Yong Sung Kwon, Hyuk Lee, Kyung Hwa Cho
SJR Q1Remote Sensing of Environment
Industrial and Manufacturing EngineeringEnvironmental Science
7
Article|145 citations·2011
Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural networkKyung Hwa Cho, Suthipong Sthiannopkao, Yakov Pachepsky, Kyoung‐Woong Kim, Joon Ha Kim
SJR Q1Water Research
Environmental ChemistryEnvironmental Science
8
Review|143 citations·2016
Modeling fate and transport of fecally-derived microorganisms at the watershed scale: State of the science and future opportunitiesKyung Hwa Cho, Yakov Pachepsky, David M. Oliver, Richard Muirhead, Yongeun Park, Richard S. Quilliam, Daniel R. Shelton
SJR Q1Water ResearchOA
Natural waters serve as habitat for a wide range of microorganisms, a proportion of which may be derived from fecal material. A number of watershed models have been developed to understand and predict the fate and transport of fecal microorganisms within complex watersheds, as well as to determine whether microbial water quality standards can be satisfied under site-specific meteorological and/or management conditions. The aim of this review is to highlight and critically evaluate developments i
Water Science and TechnologyEnvironmental Science
9
Article|129 citations·2022
Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite greenZeeshan Haider Jaffari, Ather Abbas, Sze–Mun Lam, Sanghun Park, Kangmin Chon, Eun-Sik Kim, Kyung Hwa Cho
SJR Q1Journal of Hazardous Materials
Renewable Energy, Sustainability and the EnvironmentEnergy
10
Article|122 citations·2020
A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watershedsSang‐Soo Baek, Mayzonee Ligaray, JongCheol Pyo, Jong-Pyo Park, Joo‐Hyon Kang, Yakov Pachepsky, Jong Ahn Chun, Kyung Hwa Cho
SJR Q1Journal of Hydrology
Environmental EngineeringEnvironmental Science
11
Article|118 citations·2013
STAT3 mediates TGF-β1-induced TWIST1 expression and prostate cancer invasionKyung Hwa Cho, Kang Jin Jeong, Shang Cheul Shin, Jaeku Kang, Chang Gyo Park, Hoi Young Lee
SJR Q1Cancer Letters
Molecular BiologyBiochemistry, Genetics and Molecular Biology
12
Article|117 citations·2010
Release of Escherichia coli from the bottom sediment in a first-order creek: Experiment and reach-specific modelingKyung Hwa Cho, Yakov Pachepsky, Joon Ha Kim, Andrey Guber, Daniel R. Shelton, R. Rowland
SJR Q1Journal of Hydrology
Water Science and TechnologyEnvironmental Science
13
Article|115 citations·2020
Using convolutional neural network for predicting cyanobacteria concentrations in river waterJongCheol Pyo, Lan Joo Park, Yakov Pachepsky, Sang‐Soo Baek, Kyunghyun Kim, Kyung Hwa Cho
SJR Q1Water Research
Water Science and TechnologyEnvironmental Science
14
Article|111 citations·2015
A multivariate study for characterizing particulate matter (PM10, PM2.5, and PM1) in Seoul metropolitan subway stations, KoreaSoon-Bark Kwon, Wootae Jeong, Duckshin Park, Ki‐Tae Kim, Kyung Hwa Cho
SJR Q1Journal of Hazardous MaterialsOA
Health, Toxicology and MutagenesisEnvironmental Science
15
Article|105 citations·2021
Prediction of biogas production in anaerobic co-digestion of organic wastes using deep learning modelsKwanho Jeong, Ather Abbas, Jingyeong Shin, Moon Son, Young Mo Kim, Kyung Hwa Cho
SJR Q1Water Research
Electrical and Electronic EngineeringEngineering
Research Areas
Water Science and TechnologyEnvironmental EngineeringBiomedical EngineeringOceanographyPollutionEnvironmental Chemistry