사진

  • Scopus
  • Google Citations

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
This table browses all dspace content
Issue DateTitleAuthor(s)TypeViewAltmetrics
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 alARTICLE80 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 alARTICLE94 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 alARTICLE717 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 alARTICLE72 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 alARTICLE38 Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning
2022-11Application of airborne hyperspectral imagery to retrieve spatiotemporal CDOM distribution using machine learning in a reservoirKim, Jinuk; Jang, Wonjin; Kim, Jin Hwi, et alARTICLE218 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 alARTICLE123 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 alARTICLE165 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 alARTICLE138 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 alARTICLE144 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 alARTICLE106 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 alARTICLE66 Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment br
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 alARTICLE208 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 alARTICLE155 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 alARTICLE174 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 alARTICLE123 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 alARTICLE115 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 alARTICLE351 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 alARTICLE222 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 alARTICLE103 Interactions of E. coli with algae and aquatic vegetation in natural waters

MENU