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Cho, Kyung Hwa (조경화)

Department: Department of Urban and Environmental Engineering(도시환경공학과)

Website: https://weilunist.creatorlink.net/

Lab: Water-Environmental Informatics Lab. (물환경정보학 연구실)

Research Keywords: 환경 모니터링, 환경 모델링, Water Quality Monitoring, Water Quality Modeling

Research Interests: Our Water Environment Informatics Lab has been conducting research on various topics such as remote water quality monitoring, data-based modeling, and low energy water treatment systems. We have been pioneering new research areas by combining in-depth considerations on water environment systems and various information processing technologies. In particular, by actively introducing artificial intelligence-related technologies to environmental issues, we have been conducting various analysis and prediction studies that have not been attempted before. Recent research topics are as follows.1. Water quality monitoring using hyperspectral sensors mounted on drones/aircrafts2. Development and optimization of a low-energy seawater desalination system using seawater batteries3. Water quality prediction and water treatment process optimization using deep learning techniques

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Issue Date	Title	Author(s)	Type	View	Altmetrics
2023-05	Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning	Yun, Daeun; Kang, Daeho; Cho, Kyung Hwa, et al	ARTICLE	110	Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning
2023-04	Comparison of different machine learning algorithms to estimate liquid level for bioreactor management	Yu, Sung Il; Rhee, Chaeyoung; Cho, Kyung Hwa, et al	ARTICLE	1194	Comparison of different machine learning algorithms to estimate liquid level for bioreactor management
2023-04	Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions	Jaffari, Zeeshan Haider; Abbas, Ather; Umer, Muhammed, et al	ARTICLE	22	Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions
2023-03	An autopsy study of hollow fiber and multibore ultrafiltration membranes from a pilot-scale ultra high-recovery filtration system for surface water treatment	Lee, Yong-Gu; Shin, Jaegwan; Kim, Seung Joon, et al	ARTICLE	193	An autopsy study of hollow fiber and multibore ultrafiltration membranes from a pilot-scale ultra high-recovery filtration system for surface water treatment
2023-02	Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay	Lee, Jiye; Abbas, Ather; McCarty, Gregory W., et al	ARTICLE	146	Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay
2023-02	Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning	Kwon, Do Hyuck; Hong, Seok Min; Abbas, Ather, et al	ARTICLE	58	Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning
2023-01	Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green	Jaffari, Zeeshan Haider; Abbas, Ather; Lam, Sze-Mun, et al	ARTICLE	333	Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green
2023-01	An open-source deep learning model for predicting effluent concentration in capacitive deionization	Son, Moon; Yoon, Nakyung; Park, Sanghun, et al	ARTICLE	354	An open-source deep learning model for predicting effluent concentration in capacitive deionization
2022-12	Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery	Hong, Seok Min; Cho, Kyung Hwa; Park, Sanghyun, et al	ARTICLE	975	Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery
2022-12	Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions	Park, Sanghun; Shim, Jaegyu; Yoon, Nakyung, et al	ARTICLE	271	Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions
2022-12	Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning	Park, Sanghun; Son, Moon; Shim, Jaegyu, et al	ARTICLE	219	Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning
2022-12	Automation of membrane capacitive deionization process using reinforcement learning	Yoon, Nakyung; Park, Sanghun; Son, Moon, et al	ARTICLE	269	Automation of membrane capacitive deionization process using reinforcement learning
2022-11	Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir	Kim, Jinuk; Jang, Wonjin; Kim, Jin Hwi, et al	ARTICLE	417	Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir
2022-10	Construction of delaminated Ti3C2 MXene/NiFe2O4/V2O5 ternary composites for expeditious pollutant degradation and bactericidal property	Lam, Sze-Mun; Choong, Man-Kit; Sin, Jin-Chung, et al	ARTICLE	284	Construction of delaminated Ti3C2 MXene/NiFe2O4/V2O5 ternary composites for expeditious pollutant degradation and bactericidal property
2022-09	Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forest	Park, Sanghun; Angeles, Anne Therese; Son, Moon, et al	ARTICLE	251	Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forest
2022-08	Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants	Quang Viet Ly; Viet Hung Truong; Ji, Bingxuan, et al	ARTICLE	237	Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants
2022-08	Membrane capacitive deionization model including fouling indexes obtained via real-time fouling layer measurements	Yoon, Nakyung; Park, Sanghun; Shim, Jaegyu, et al	ARTICLE	76	Membrane capacitive deionization model including fouling indexes obtained via real-time fouling layer measurements
2022-06	Chemical accidents in freshwater: Development of forecasting system for drinking water resources	Kim, Soobin; Kim, Minjeong; Kim, Hyein, et al	ARTICLE	240	Chemical accidents in freshwater: Development of forecasting system for drinking water resources
2022-06	Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus level	Baek, Sang-Soo; Jung, Eun-Young; Pyo, JongCheol, et al	ARTICLE	201	Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus level
2022-06	Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment br	Son, Moon; Shim, Jaegyu; Park, Sanghun, et al	ARTICLE	139	Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment br

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