| 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 |
| Developing a deep learning model for the simulation of micro-pollutants in a watershed D Yun, A Abbas, J Jeon, M Ligaray, SS Baek, KH Cho Journal of Cleaner Production 300, 126858, 2021 | 26 | 2021 |
| Automatic classification of microplastics and natural organic matter mixtures using a deep learning model S Lee, H Jeong, SM Hong, D Yun, J Lee, E Kim, KH Cho Water Research 246, 120710, 2023 | 20 | 2023 |
| Characterization of micropollutants in urban stormwater using high-resolution monitoring and machine learning D Yun, D Kang, KH Cho, SS Baek, J Jeon Water Research 235, 119865, 2023 | 14 | 2023 |
| Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling K Phetanan, SM Hong, D Yun, J Lee, S Chotpantarat, H Jeong, KH Cho Journal of Hydrology: Regional Studies 53, 101820, 2024 | 13 | 2024 |
| Autonomous calibration of EFDC for predicting chlorophyll-a using reinforcement learning and a real-time monitoring system SM Hong, A Abbas, S Kim, DH Kwon, N Yoon, D Yun, S Lee, ... Environmental Modelling & Software 168, 105805, 2023 | 13 | 2023 |
| Analysis of micropollutants in a marine outfall using network analysis and decision tree SS Baek, D Yun, JC Pyo, D Kang, KH Cho, J Jeon Science of The Total Environment 806, 150938, 2022 | 12 | 2022 |
| Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models S Kim, E Lee, HT Hwang, JC Pyo, D Yun, SS Baek, KH Cho Water Research X 23, 100228, 2024 | 11 | 2024 |
| Simulation models of microbial community, pH, and volatile fatty acids of anaerobic digestion developed by machine learning SI Yu, H Jeong, J Shin, SG Shin, A Abbas, D Yun, H Bae, KH Cho Journal of Water Process Engineering 60, 105225, 2024 | 10 | 2024 |
| A novel method for micropollutant quantification using deep learning and multi-objective optimization D Yun, D Kang, J Jang, AT Angeles, JC Pyo, J Jeon, SS Baek, KH Cho Water Research 212, 118080, 2022 | 10 | 2022 |
| Profiling emerging micropollutants in urban stormwater runoff using suspect and non-target screening via high-resolution mass spectrometry D Kang, D Yun, KH Cho, SS Baek, J Jeon Chemosphere 352, 141402, 2024 | 8 | 2024 |
| Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland J Lee, D Kim, S Hong, D Yun, D Kwon, RL Hill, F Gao, X Zhang, KH Cho, ... Science of The Total Environment 954, 176256, 2024 | 1 | 2024 |
| Corrigendum to" Comparative efficiency of the SWAT model and a deep learning model in estimating nitrate loads at the Tuckahoe creek watershed, Maryland"[Sci. Total Environ … J Lee, D Kim, S Hong, D Yun, D Kwon, RL Hill, F Gao, X Zhang, KH Cho, ... The Science of the total environment 956, 177316, 2024 | | 2024 |
| Comparing Performance of Data-driven Deep Learning and Mechanistic Models for Simulating Nitrate Loads in a Maryland Watershed J Lee, F Gao, X Zhang, KIM Dongho, S Hong, D Yun, DH Kwon, RL Hill, ... AGU24, 2024 | | 2024 |
| Comparison of Deep Learning and Mechanistic Models for Nitrate Load Simulations. J Lee, F Gao, X Zhang, D Kim, S Hong, D Yun, D Kwon, RL Hill, KH Cho, ... ASA, CSSA, SSSA International Annual Meeting, 2024 | | 2024 |
| Comparison of SWAT and a deep learning model in nitrate load simulation at the Tuckahoe creek watershed in the United States J Lee, D Kim, S Hong, D Yun, D Kwon, R Hill, Y Pachepsky, F Gao, ... EGU General Assembly Conference Abstracts, 6622, 2024 | | 2024 |
| Application of Reinforcement Learning to Calibrate Cyanobacteria using Daily Water Quality Parameters in EFDC Model S Hong, K Hwa Cho, A Abbas, S KIM, DH Kwon, N Yoon, D Yun, S Lee AGU Fall Meeting Abstracts 2022, H15L-0942, 2022 | | 2022 |
| Modeling the First Flush Effect of Urban Micropollutants with Sensitivity Analysis and Uncertainty Analysis D Yun, SM Hong, S Kim, D Kang, J Jeon, H Kim, SS Baek, K Cho Available at SSRN 5168467, 0 | | |
Kyung Hwa ChoKorea UniversityZweryfikowany adres z korea.ac.kr