| Development of water level prediction models using machine learning in wetlands: A case study of Upo wetland in South Korea C Choi, J Kim, H Han, D Han, HS Kim Water 12 (1), 93, 2019 | 93 | 2019 |
| Deep Learning with Long Short Term Memory Based Sequence-to-Sequence Model for Rainfall-Runoff Simulation H Han, C Choi, J Jung, HS Kim Water 13 (4), 437, 2021 | 44 | 2021 |
| Use of a high-resolution-satellite-based precipitation product in mapping continental-scale rainfall erosivity: A case study of the United States J Kim, H Han, B Kim, H Chen, JH Lee CATENA 193, 104602, 2020 | 42 | 2020 |
| Data-driven approaches for runoff prediction using distributed data H Han, RR Morrison Stochastic Environmental Research and Risk Assessment 36 (8), 2153-2171, 2022 | 38 | 2022 |
| Hybrid machine learning framework for hydrological assessment J Kim, H Han, LE Johnson, S Lim, R Cifelli Journal of Hydrology 577, 123913, 2019 | 38 | 2019 |
| Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea J Kim, H Han Atmospheric Research 258, 105650, 2021 | 37 | 2021 |
| Improved runoff forecasting performance through error predictions using a deep-learning approach H Han, RR Morrison Journal of Hydrology 608, 127653, 2022 | 33 | 2022 |
| Modeling streamflow enhanced by precipitation from atmospheric river using the NOAA national water model: a case study of the Russian river basin for February 2004 H Han, J Kim, V Chandrasekar, J Choi, S Lim Atmosphere 10 (8), 466, 2019 | 27 | 2019 |
| Machine Learning-Based Small Hydropower Potential Prediction under Climate Change J Jung, H Han, K Kim, HS Kim Energies 14 (12), 3643, 2021 | 23 | 2021 |
| Modeling the runoff reduction effect of low impact development installations in an industrial area, South Korea J Kim, J Lee, Y Song, H Han, J Joo Water 10 (8), 967, 2018 | 20 | 2018 |
| An experiment on reservoir representation schemes to improve hydrologic prediction: coupling the national water model with the HEC-ResSim J Kim, L Read, LE Johnson, D Gochis, R Cifelli, H Han Hydrological Sciences Journal 65 (10), 1652-1666, 2020 | 19 | 2020 |
| Application of Deep Learning Models and Network Method for Comprehensive Air-Quality Index Prediction D Kim, H Han, W Wang, Y Kang, H Lee, HS Kim Applied Sciences 12 (13), 6699, 2022 | 18 | 2022 |
| Improvement of Deep Learning Models for River Water Level Prediction Using Complex Network Method D Kim, H Han, W Wang, HS Kim Water 14 (3), 466, 2022 | 17 | 2022 |
| Multiple-Depth Soil Moisture Estimates Using Artificial Neural Network and Long Short-Term Memory Models H Han, C Choi, J Kim, RR Morrison, J Jung, HS Kim Water 13 (18), 2584, 2021 | 17 | 2021 |
| Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow H Han, C Choi, J Jung, HS Kim Journal of Korea Water Resources Association 54 (3), 157-166, 2021 | 16 | 2021 |
| Development of a Deep Learning-Based Prediction Model for Water Consumption at the Household Level J Kim, H Lee, M Lee, H Han, D Kim, HS Kim Water 14 (9), 1512, 2022 | 15 | 2022 |
| Application of AI-based models for flood water level forecasting and flood risk classification D Kim, J Park, H Han, H Lee, HS Kim, S Kim KSCE Journal of Civil Engineering 27 (7), 3163-3174, 2023 | 14 | 2023 |
| Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea J Kwak, H Han, S Kim, HS Kim Stochastic Environmental Research and Risk Assessment 36 (6), 1615-1629, 2022 | 10 | 2022 |
| Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario J Kim, M Lee, H Han, D Kim, Y Bae, HS Kim Sustainability 14 (8), 4719, 2022 | 10 | 2022 |
| Flood risk assessment using an indicator based approach combined with flood risk maps and grid data W Wang, D Kim, H Han, KT Kim, S Kim, HS Kim Journal of Hydrology 627, 130396, 2023 | 7 | 2023 |
