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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
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 alARTICLE294 Estimation of cyanobacteria pigments in the main rivers of South Korea using spatial attention convolutional neural network with hyperspectral imagery
2022-09Predicting the salt adsorption capacity of different capacitive deionization electrodes using random forestPark, Sanghun; Angeles, Anne Therese; Son, Moon, et alARTICLE26 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 alARTICLE20 Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants
2022-06Chemical accidents in freshwater: Development of forecasting system for drinking water resourcesKim, Soobin; Kim, Minjeong; Kim, Hyein, et alARTICLE79 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 alARTICLE41 Hierarchical deep learning model to simulate phytoplankton at phylum/ class and genus levels and zooplankton at the genus level
2022-05Abundance and diversity of antibiotic resistance genes and bacterial communities in the western Pacific and Southern OceansJang, Jiyi; Park, Jiyeon; Hwang, Chung Yeon, et alARTICLE119 Abundance and diversity of antibiotic resistance genes and bacterial communities in the western Pacific and Southern Oceans
2022-04A novel method for micropollutant quantification using deep learning and multi-objective optimizationYun, Daeun; Kang, Daeho; Jang, Jiyi, et alARTICLE82 A novel method for micropollutant quantification using deep learning and multi-objective optimization
2022-04Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven ApproachJang, Wonjin; Park, Yongeun; Pyo, JongCheol, et alARTICLE77 Optimal Band Selection for Airborne Hyperspectral Imagery to Retrieve a Wide Range of Cyanobacterial Pigment Concentration Using a Data-Driven Approach
2022-04Dynamic calibration of phytoplankton blooms using the modified SWAT modelLee, Jiye; Woo, So-Young; Kim, Yong-Won, et alARTICLE60 Dynamic calibration of phytoplankton blooms using the modified SWAT model
2022-04AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methodsAbbas, Ather; Boithias, Laurie; Pachepsky, Yakov, et alARTICLE47 AI4Water v1.0: an open-source python package for modeling hydrological time series using data-driven methods
2022-02When river water meets seawater: Insights into primary marine aerosol productionPark, Jiyeon; Jang, Jiyi; Yoon, Young Jun, et alARTICLE231 When river water meets seawater: Insights into primary marine aerosol production
2022-02Analysis of micropollutants in a marine outfall using network analysis and decision treeBaek, Sang-Soo; Yun, Daeun; Pyo, JongCheol, et alARTICLE131 Analysis of micropollutants in a marine outfall using network analysis and decision tree
2022-02Interactions of E. coli with algae and aquatic vegetation in natural watersCho, Kyung Hwa; Wolny, Jennifer; Kase, Julie A., et alARTICLE46 Interactions of E. coli with algae and aquatic vegetation in natural waters
2022-01Drone-borne sensing of major and accessory pigments in algae using deep learning modelingPyo, JongCheol; Hong, Seok Min; Jang, Jiyi, et alARTICLE109 Drone-borne sensing of major and accessory pigments in algae using deep learning modeling
2022-01Seawater battery desalination with a reverse osmosis membrane for simultaneous brine treatment and energy storagePark, Sanghun; Kim, Namhyeok; Kim, Youngsik, et alARTICLE44 Seawater battery desalination with a reverse osmosis membrane for simultaneous brine treatment and energy storage
2021-12Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plantJeong, Kwanho; Son, Moon; Yoon, Nakyung, et alARTICLE231 Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant
2021-12Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling methodKim, Jin Hwi; Shin, Jae-Ki; Lee, Hankyu, et alARTICLE118 Improving the performance of machine learning models for early warning of harmful algal blooms using an adaptive synthetic sampling method
2021-12In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based modelsAbbas, Ather; Baek, Sangsoo; Silvera, Norbert, et alARTICLE109 In-stream Escherichia coli modeling using high-temporal-resolution data with deep learning and process-based models
2021-11Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning modelsHong, Seok Min; Baek,Sang-Soo; Yun, Daeun, et alARTICLE163 Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models
2021-11Application of Machine Learning for eutrophication analysis and algal bloom prediction in an urban river: A 10-year study of the Han River, South KoreaLy, Quang Viet; Nguyen, Xuan Cuong; Lê, Ngoc C., et alARTICLE170 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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