Heechan Han
Cited by
Cited by
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
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
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
Data-driven approaches for runoff prediction using distributed data
H Han, RR Morrison
Stochastic Environmental Research and Risk Assessment 36 (8), 2153-2171, 2022
Hybrid machine learning framework for hydrological assessment
J Kim, H Han, LE Johnson, S Lim, R Cifelli
Journal of Hydrology 577, 123913, 2019
Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea
J Kim, H Han
Atmospheric Research 258, 105650, 2021
Improved runoff forecasting performance through error predictions using a deep-learning approach
H Han, RR Morrison
Journal of Hydrology 608, 127653, 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
Machine Learning-Based Small Hydropower Potential Prediction under Climate Change
J Jung, H Han, K Kim, HS Kim
Energies 14 (12), 3643, 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
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
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
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
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
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
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
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
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
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
Determining the Risk Level of Heavy Rain Damage by Region in South Korea
J Kim, D Kim, M Lee, H Han, HS Kim
Water 14 (2), 219, 2022
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