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Kyung Hwa Cho

Kyung Hwa Cho

D-Index & Metrics

Engineering and Technology

D-Index
51
Citations
9019
World Ranking
3901
National Ranking
89

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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