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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
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	294	Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery
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	26	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	20	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	79	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	41	Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus level
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	119	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	82	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	77	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	60	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	47	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	231	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	131	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	46	Interactions of E. coli with algae and aquatic vegetation in natural waters
2022-01	Drone-borne sensing of major and accessory pigments in algae using deep learning modeling	Pyo, JongCheol; Hong, Seok Min; Jang, Jiyi, et al	ARTICLE	109	Drone-borne sensing of major and accessory pigments in algae using deep learning modeling
2022-01	Seawater battery desalination with a reverse osmosis membrane for simultaneous brine treatment and energy storage	Park, Sanghun; Kim, Namhyeok; Kim, Youngsik, et al	ARTICLE	44	Seawater battery desalination with a reverse osmosis membrane for simultaneous brine treatment and energy storage
2021-12	Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant	Jeong, Kwanho; Son, Moon; Yoon, Nakyung, et al	ARTICLE	231	Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant
2021-12	Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method	Kim, Jin Hwi; Shin, Jae-Ki; Lee, Hankyu, et al	ARTICLE	118	Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method
2021-12	In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models	Abbas, Ather; Baek, Sangsoo; Silvera, Norbert, et al	ARTICLE	109	In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models
2021-11	Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models	Hong, Seok Min; Baek,Sang-Soo; Yun, Daeun, et al	ARTICLE	163	Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models
2021-11	Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea	Ly, Quang Viet; Nguyen, Xuan Cuong; Lê, Ngoc C., et al	ARTICLE	170	Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South Korea

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