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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
2023-01	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	80	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	94	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	717	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	72	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	38	Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning
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	218	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	123	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	165	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	138	Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants
2022-06	Chemical accidents in freshwater: Development of forecasting system for drinking water resources	Kim, Soobin; Kim, Minjeong; Kim, Hyein, et al	ARTICLE	144	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	106	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	66	Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment br
2022-05	Abundance and diversity of antibiotic resistance genes and bacterial communities in the western Pacific and Southern Oceans	Jang, Jiyi; Park, Jiyeon; Hwang, Chung Yeon, et al	ARTICLE	208	Abundance and diversity of antibiotic resistance genes and bacterial communities in the western Pacific and Southern Oceans
2022-04	A novel method for micropollutant quantification using deep learning and multi-objective optimization	Yun, Daeun; Kang, Daeho; Jang, Jiyi, et al	ARTICLE	155	A novel method for micropollutant quantification using deep learning and multi-objective optimization
2022-04	Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach	Jang, Wonjin; Park, Yongeun; Pyo, JongCheol, et al	ARTICLE	174	Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach
2022-04	Dynamic calibration of phytoplankton blooms using the modified SWAT model	Lee, Jiye; Woo, So-Young; Kim, Yong-Won, et al	ARTICLE	123	Dynamic calibration of phytoplankton blooms using the modified SWAT model
2022-04	AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods	Abbas, Ather; Boithias, Laurie; Pachepsky, Yakov, et al	ARTICLE	115	AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
2022-02	When river water meets seawater: Insights into primary marine aerosol production	Park, Jiyeon; Jang, Jiyi; Yoon, Young Jun, et al	ARTICLE	351	When river water meets seawater: Insights into primary marine aerosol production
2022-02	Analysis of micropollutants in a marine outfall using network analysis and decision tree	Baek, Sang-Soo; Yun, Daeun; Pyo, JongCheol, et al	ARTICLE	222	Analysis of micropollutants in a marine outfall using network analysis and decision tree
2022-02	Interactions of E. coli with algae and aquatic vegetation in natural waters	Cho, Kyung Hwa; Wolny, Jennifer; Kase, Julie A., et al	ARTICLE	103	Interactions of E. coli with algae and aquatic vegetation in natural waters

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