| Prediction of water level and water quality using a CNN-LSTM combined deep learning approach SS Baek, J Pyo, JA Chun Water 12 (12), 3399, 2020 | 216 | 2020 |
| A convolutional neural network regression for quantifying cyanobacteria using hyperspectral imagery JC Pyo, H Duan, S Baek, MS Kim, T Jeon, YS Kwon, H Lee, KH Cho Remote Sensing of Environment 233, 111350, 2019 | 169 | 2019 |
| Estimation of heavy metals using deep neural network with visible and infrared spectroscopy of soil JC Pyo, SM Hong, YS Kwon, MS Kim, KH Cho Science of the Total Environment 741, 140162, 2020 | 151 | 2020 |
| A novel water quality module of the SWMM model for assessing low impact development (LID) in urban watersheds SS Baek, M Ligaray, J Pyo, JP Park, JH Kang, Y Pachepsky, JA Chun, ... Journal of Hydrology 586, 124886, 2020 | 110 | 2020 |
| Drone-based hyperspectral remote sensing of cyanobacteria using vertical cumulative pigment concentration in a deep reservoir YS Kwon, JC Pyo, YH Kwon, H Duan, KH Cho, Y Park Remote Sensing of Environment 236, 111517, 2020 | 106 | 2020 |
| Using convolutional neural network for predicting cyanobacteria concentrations in river water JC Pyo, LJ Park, Y Pachepsky, SS Baek, K Kim, KH Cho Water Research 186, 116349, 2020 | 92 | 2020 |
| Deep neural networks for modeling fouling growth and flux decline during NF/RO membrane filtration S Park, SS Baek, JC Pyo, Y Pachepsky, J Park, KH Cho Journal of Membrane Science 587, 117164, 2019 | 78 | 2019 |
| Identification and enumeration of cyanobacteria species using a deep neural network SS Baek†, JC Pyo†, Y Pachepsky, Y Park, M Ligaray, CY Ahn, YH Kim, ... Ecological Indicators 115, 106395, 2020 | 72 | 2020 |
| Modeling seasonal variability of fecal coliform in natural surface waters using the modified SWAT KH Cho, YA Pachepsky, M Kim, JC Pyo, MH Park, YM Kim, JW Kim, ... Journal of Hydrology 535, 377-385, 2016 | 72 | 2016 |
| 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 QV Ly, XC Nguyen, NC Lê, TD Truong, THT Hoang, TJ Park, T Maqbool, ... Science of The Total Environment 797, 149040, 2021 | 67 | 2021 |
| Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea Y Park, JC Pyo, YS Kwon, YK Cha, H Lee, T Kang, KH Cho Water research 126, 319-328, 2017 | 67 | 2017 |
| Comparative studies of different imputation methods for recovering streamflow observation M Kim, S Baek, M Ligaray, J Pyo, M Park, KH Cho Water 7 (12), 6847-6860, 2015 | 61 | 2015 |
| High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery JC Pyo, M Ligaray, YS Kwon, MH Ahn, K Kim, H Lee, T Kang, SB Cho, ... Remote Sensing 10 (8), 1180, 2018 | 58 | 2018 |
| Cyanobacteria cell prediction using interpretable deep learning model with observed, numerical, and sensing data assemblage JC Pyo, KH Cho, K Kim, SS Baek, G Nam, S Park Water Research 203, 117483, 2021 | 52 | 2021 |
| Monitoring coastal chlorophyll-a concentrations in coastal areas using machine learning models YS Kwon, SH Baek, YK Lim, JC Pyo, M Ligaray, Y Park, KH Cho Water 10 (8), 1020, 2018 | 49 | 2018 |
| Drone-borne sensing of major and accessory pigments in algae using deep learning modeling JC Pyo, SM Hong, J Jang, S Park, J Park, JH Noh, KH Cho GIScience & Remote Sensing 59 (1), 310-332, 2022 | 35 | 2022 |
| Replacing the internal standard to estimate micropollutants using deep and machine learning SS Baek, Y Choi, J Jeon, JC Pyo, J Park, KH Cho Water Research 188, 116535, 2021 | 35 | 2021 |
| Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models SM Hong, SS Baek, D Yun, YH Kwon, H Duan, JC Pyo*, KH Cho* Science of The Total Environment 794, 148592, 2021 | 31 | 2021 |
| An integrative remote sensing application of stacked autoencoder for atmospheric correction and cyanobacteria estimation using hyperspectral imagery JC Pyo, H Duan, M Ligaray, M Kim, S Baek, YS Kwon, H Lee, T Kang, ... Remote Sensing 12 (7), 1073, 2020 | 31 | 2020 |
| Optimizing Semi-Analytical Algorithms for Estimating Chlorophyll-a and Phycocyanin Concentrations in Inland Waters in Korea JC Pyo, Y Pachepsky, SS Baek, YS Kwon, MJ Kim, H Lee, S Park, YK Cha, ... Remote Sensing 9 (6), 542, 2017 | 27 | 2017 |
Kyung Hwa ChoKorea Universityبريد إلكتروني تم التحقق منه على korea.ac.kr