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JongCheol Pyo

Showing results (1-10 of 23) with videos related to

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Journal of Hazardous Materials|June 14, 2024
Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matterCharles O Esu, JongCheol Pyo, Kuk Cho
Water Research|May 14, 2025
Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality in South KoreaJunGi Moon, SangJin Jung, SungMin Suh, et al.
The Science of the Total Environment|November 13, 2024
Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South KoreaSungMin Suh, JunGi Moon, Sangjin Jung, et al.
Water Research|November 30, 2024
Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi modelOkon Dominic Ekpe, Haeran Moon, JongCheol Pyo, et al.
The Science of the Total Environment|October 1, 2024
Classifying eutrophication spatio-temporal dynamics in river systems using deep learning techniqueDukyeong Lee, JunGi Moon, SangJin Jung, et al.
The Science of the Total Environment|October 16, 2021
Analysis of micropollutants in a marine outfall using network analysis and decision treeSang-Soo Baek, Daeun Yun, JongCheol Pyo, et al.
The Science of the Total Environment|September 5, 2020
Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soilJongCheol Pyo, Seok Min Hong, Yong Sung Kwon, et al.
Water Research|November 4, 2020
Replacing the internal standard to estimate micropollutants using deep and machine learningSang-Soo Baek, Younghun Choi, Junho Jeon, et al.
Water Research|August 13, 2021
Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblageJongCheol Pyo, Kyung Hwa Cho, Kyunghyun Kim, et al.
Water Research|September 4, 2020
Using convolutional neural network for predicting cyanobacteria concentrations in river waterJongCheol Pyo, Lan Joo Park, Yakov Pachepsky, et al.
Pageof 3

Showing results (1-10 of 23) with videos related to

Sort By:
Pageof 3
Journal of Hazardous Materials|June 14, 2024
Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matterCharles O Esu, JongCheol Pyo, Kuk Cho
Water Research|May 14, 2025
Development of deep learning quantization framework for remote sensing edge device to estimate inland water quality in South KoreaJunGi Moon, SangJin Jung, SungMin Suh, et al.
The Science of the Total Environment|November 13, 2024
Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South KoreaSungMin Suh, JunGi Moon, Sangjin Jung, et al.
Water Research|November 30, 2024
Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi modelOkon Dominic Ekpe, Haeran Moon, JongCheol Pyo, et al.
The Science of the Total Environment|October 1, 2024
Classifying eutrophication spatio-temporal dynamics in river systems using deep learning techniqueDukyeong Lee, JunGi Moon, SangJin Jung, et al.
The Science of the Total Environment|October 16, 2021
Analysis of micropollutants in a marine outfall using network analysis and decision treeSang-Soo Baek, Daeun Yun, JongCheol Pyo, et al.
The Science of the Total Environment|September 5, 2020
Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soilJongCheol Pyo, Seok Min Hong, Yong Sung Kwon, et al.
Water Research|November 4, 2020
Replacing the internal standard to estimate micropollutants using deep and machine learningSang-Soo Baek, Younghun Choi, Junho Jeon, et al.
Water Research|August 13, 2021
Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblageJongCheol Pyo, Kyung Hwa Cho, Kyunghyun Kim, et al.
Water Research|September 4, 2020
Using convolutional neural network for predicting cyanobacteria concentrations in river waterJongCheol Pyo, Lan Joo Park, Yakov Pachepsky, et al.
Pageof 3