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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 DateTitleAuthor(s)TypeViewAltmetrics
2023-05Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learningYun, Daeun; Kang, Daeho; Cho, Kyung Hwa, et alARTICLE110 Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning
2023-04Comparison of different machine learning algorithms to estimate liquid level for bioreactor managementYu, Sung Il; Rhee, Chaeyoung; Cho, Kyung Hwa, et alARTICLE1194 Comparison of different machine learning algorithms to estimate liquid level for bioreactor management
2023-04Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ionsJaffari, Zeeshan Haider; Abbas, Ather; Umer, Muhammed, et alARTICLE22 Crystal graph convolution neural networks for fast and accurate prediction of adsorption ability of Nb2CTx towards Pb(ii) and Cd(ii) ions
2023-03An autopsy study of hollow fiber and multibore ultrafiltration membranes from a pilot-scale ultra high-recovery filtration system for surface water treatmentLee, Yong-Gu; Shin, Jaegwan; Kim, Seung Joon, et alARTICLE193 An autopsy study of hollow fiber and multibore ultrafiltration membranes from a pilot-scale ultra high-recovery filtration system for surface water treatment
2023-02Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake BayLee, Jiye; Abbas, Ather; McCarty, Gregory W., et alARTICLE146 Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay
2023-02Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learningKwon, Do Hyuck; Hong, Seok Min; Abbas, Ather, et alARTICLE58 Inland harmful algal blooms (HABs) modeling using internet of things (IoT) system and deep learning
2023-01Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite greenJaffari, Zeeshan Haider; Abbas, Ather; Lam, Sze-Mun, et alARTICLE333 Machine learning approaches to predict the photocatalytic performance of bismuth ferrite-based materials in the removal of malachite green
2023-01An open-source deep learning model for predicting effluent concentration in capacitive deionizationSon, Moon; Yoon, Nakyung; Park, Sanghun, et alARTICLE354 An open-source deep learning model for predicting effluent concentration in capacitive deionization
2022-12Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imageryHong, Seok Min; Cho, Kyung Hwa; Park, Sanghyun, et alARTICLE975 Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery
2022-12Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditionsPark, Sanghun; Shim, Jaegyu; Yoon, Nakyung, et alARTICLE271 Deep reinforcement learning in an ultrafiltration system: Optimizing operating pressure and chemical cleaning conditions
2022-12Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaningPark, Sanghun; Son, Moon; Shim, Jaegyu, et alARTICLE219 Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning
2022-12Automation of membrane capacitive deionization process using reinforcement learningYoon, Nakyung; Park, Sanghun; Son, Moon, et alARTICLE269 Automation of membrane capacitive deionization process using reinforcement learning
2022-11Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoirKim, Jinuk; Jang, Wonjin; Kim, Jin Hwi, et alARTICLE417 Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoir
2022-10Construction of delaminated Ti3C2 MXene/NiFe2O4/V2O5 ternary composites for expeditious pollutant degradation and bactericidal propertyLam, Sze-Mun; Choong, Man-Kit; Sin, Jin-Chung, et alARTICLE284 Construction of delaminated Ti3C2 MXene/NiFe2O4/V2O5 ternary composites for expeditious pollutant degradation and bactericidal property
2022-09Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forestPark, Sanghun; Angeles, Anne Therese; Son, Moon, et alARTICLE251 Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forest
2022-08Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plantsQuang Viet Ly; Viet Hung Truong; Ji, Bingxuan, et alARTICLE237 Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants
2022-08Membrane capacitive deionization model including fouling indexes obtained via real-time fouling layer measurementsYoon, Nakyung; Park, Sanghun; Shim, Jaegyu, et alARTICLE76 Membrane capacitive deionization model including fouling indexes obtained via real-time fouling layer measurements
2022-06Chemical accidents in freshwater: Development of forecasting system for drinking water resourcesKim, Soobin; Kim, Minjeong; Kim, Hyein, et alARTICLE240 Chemical accidents in freshwater: Development of forecasting system for drinking water resources
2022-06Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus levelBaek, Sang-Soo; Jung, Eun-Young; Pyo, JongCheol, et alARTICLE201 Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus level
2022-06Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment brSon, Moon; Shim, Jaegyu; Park, Sanghun, et alARTICLE139 Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment br

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