| An interpretable machine learning method for supporting ecosystem management: Application to species distribution models of freshwater macroinvertebrates YK Cha, J Shin, BG Go, DS Lee, YW Kim, TH Kim, YS Park Journal of Environmental Management 291, 112719, 2021 | 77 | 2021 |
| Effects of class imbalance on resampling and ensemble learning for improved prediction of cyanobacteria blooms J Shin, S Yoon, YW Kim, T Kim, BG Go, YK Cha Ecological informatics 61, 101202, 2021 | 49 | 2021 |
| Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters YW Kim, TH Kim, J Shin, DS Lee, YS Park, Y Kim, YK Cha Ecological Indicators 137, 108737, 2022 | 47 | 2022 |
| Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach TH Kim, J Shin, DY Lee, YW Kim, E Na, JH Park, C Lim, YK Cha Water Research 215, 118289, 2022 | 39 | 2022 |
| Forecasting abrupt depletion of dissolved oxygen in urban streams using discontinuously measured hourly time‐series data YW Kim, TH Kim, J Shin, BG Go, M Lee, JH Lee, J Koo, KH Cho, YK Cha Water Resources Research 57 (4), e2020WR029188, 2021 | 38 | 2021 |
| Deep learning-based retrieval of cyanobacteria pigment in inland water for in-situ and airborne hyperspectral data I Yim, J Shin, H Lee, S Park, G Nam, T Kang, KH Cho, YK Cha Ecological Indicators 110, 105879, 2020 | 27 | 2020 |
| Willingness to pay for improved water supply services based on asset management: A contingent valuation study in South Korea T Kim, J Shin, J Hyung, K Kim, J Koo, YK Cha Water 13 (15), 2040, 2021 | 24 | 2021 |
| Machine learning-based prediction of harmful algal blooms in water supply reservoirs B Jeong, MR Chapeta, M Kim, J Kim, J Shin, YK Cha Water Quality Research Journal 57 (4), 304-318, 2022 | 19 | 2022 |
| Prediction of cyanobacteria blooms in the lower Han River (South Korea) using ensemble learning algorithms J Shin, S Yoon, Y Cha Desalination and Water Treatment 84, 31-39, 2017 | 19 | 2017 |
| Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management J Shin, SH Son, YK Cha Sustainable Cities and Society 87, 104255, 2022 | 15 | 2022 |
| Assessing land-cover effects on stream water quality in metropolitan areas using the water quality index TH Kim, YW Kim, J Shin, BG Go, YK Cha Water 12 (11), 3294, 2020 | 14 | 2020 |
| Evaluation of temperature effects on brake wear particles using clustered heatmaps J Shin, I Yim, SB Kwon, S Park, M Kim, YK Cha Environmental Engineering Research 24 (4), 680-689, 2019 | 11 | 2019 |
| Deep learning-based efficient drone-borne sensing of cyanobacterial blooms using a clique-based feature extraction approach J Shin, G Lee, TH Kim, KH Cho, SM Hong, DH Kwon, JC Pyo, YK Cha Science of The Total Environment 912, 169540, 2024 | 10 | 2024 |
| Machine learning-guided prediction of potential engineering targets for microbial production of lycopene CK Kang, J Shin, YK Cha, MS Kim, MS Choi, TH Kim, YK Park, YJ Choi Bioresource Technology 369, 128455, 2023 | 10 | 2023 |
| Data-driven modeling of freshwater aquatic systems: Status and prospects YK Cha, J Shin, YW Kim Journal of Korean Society on Water Environment 36 (6), 611-620, 2020 | 9 | 2020 |
| Learning hierarchical Bayesian networks to assess the interaction effects of controlling factors on spatiotemporal patterns of fecal pollution in streams TH Kim, DY Lee, J Shin, YW Kim, YK Cha Science of the Total Environment 812, 152520, 2022 | 6 | 2022 |
| Incorporation of feature engineering and attention mechanisms into deep learning models to develop an early warning system for harmful algal blooms TH Kim, J Shin, YK Cha Journal of Cleaner Production 414, 137564, 2023 | 5 | 2023 |
| Changes in zooplankton community in response to a shift from lentic to lotic conditions in a regulated river T Park, G Lee, J Shin, J Park, B Choi, DK Kim, YK Cha Ecological Informatics 77, 102236, 2023 | 3 | 2023 |
| Hybrid model for daily streamflow and phosphorus load prediction DY Lee, J Shin, TH Kim, S Lee, D Kim, Y Park, YK Cha Water Science & Technology 88 (4), 975-990, 2023 | 3 | 2023 |
| Generalizability evaluations of heterogeneous ensembles for river health predictions T Park, J Shin, B Park, J Moon, YK Cha Ecological Informatics 82, 102719, 2024 | 1 | 2024 |