Publications

Below is a list of my current publications.

Kim, D., Han, H., Lee, H., Kang, Y., Wang, W., & Kim, H. S. (2024). Predicting Flood Water Level Using Combined Hybrid Model of Rainfall-Runoff and AI-Based Models. KSCE Journal of Civil Engineering, 28(4), 1580-1593. https://doi.org/10.1007/s12205-023-1147-0

Wang, W. J., Kim, D., Han, H., Kim, K. T., Kim, S., & Kim, H. S. (2023). Flood risk assessment using an indicator based approach combined with flood risk maps and grid data. Journal of Hydrology, 627, 130396. https://doi.org/10.1016/j.jhydrol.2023.130396

Lee, S., Wang, W., Kim, D., Han, H., Kim, S., & Kim, H. S. (2023). Establishing meteorological drought severity considering the level of emergency water supply. Journal of Korea Water Resources Association, 56(10), 619-629. https://doi.org/10.3741/JKWRA.2023.56.10.619

Han, H., Abitew, T. A., Park, S., Green, C. H., & Jeong, J. (2023). Spatiotemporal evaluation of satellite-based precipitation products in the Colorado river basin. Journal of Hydrometeorology, 24(10), 1739-1754. https://doi.org/10.1175/JHM-D-23-0003.1

Han, H., Kim, B., Kim, K., Kim, D., & Kim, H. S. (2023). Machine learning approach for the estimation of missing precipitation data: a case study of South Korea. Water Science & Technology, 88(3), 556-571. https://doi.org/10.2166/wst.2023.237

Kim, D., Park, J., Han, H., Lee, H., Kim, H. S., & Kim, S. (2023). Application of AI-based models for flood water level forecasting and flood risk classification. KSCE Journal of Civil Engineering, 27(7), 3163-3174. https://doi.org/10.1007/s12205-023-2175-5

Kim, D., Han, H., Lee, H., Kim, H. S., & Kim, J. (2023). Development of heavy rain damage prediction technique based on optimization and ensemble method. KSCE Journal of Civil Engineering, 27(5), 2313-2326. https://doi.org/10.1007/s12205-023-2099-0

Han, H., Kim, D., Wang, W., & Kim, H. S. (2023). Dam inflow prediction using large-scale climate variability and deep learning approach: a case study in South Korea. Water Supply, 23(2), 934-947. https://doi.org/10.2166/ws.2023.012

Jung, J., & Han, H. (2022). Development of Technology for Identification of Climate Patterns during Floods Using Global Climate Model Data with Convolutional Neural Networks. Water, 14(24), 4045. https://doi.org/10.3390/w14244045

Han, H., & Morrison, R. R. (2022). Data-driven approaches for runoff prediction using distributed data. Stochastic Environmental Research and Risk Assessment, 36(8), 2153-2171. https://doi.org/10.1007/s00477-021-01993-3

Kwak, J., Han, H., Kim, S., & Kim, H. S. (2021). Is the deep-learning technique a completely alternative for the hydrological model?: A case study on Hyeongsan River Basin, Korea. Stochastic Environmental Research and Risk Assessment, 1-15. https://doi.org/10.1007/s00477-021-02094-x

Kim, J., Lee, H., Lee, M., Han, H., Kim, D., & Kim, H. S. (2022). Development of a deep learning-based prediction model for water consumption at the household level. Water, 14(9), 1512. https://doi.org/10.3390/w14091512

Han, H., & Morrison, R. R. (2022). Improved runoff forecasting performance through error predictions using a deep-learning approach. Journal of Hydrology, 608, 127653. https://doi.org/10.1016/j.jhydrol.2022.127653

Han, H., Kwak, J., Kim, D., Jung, J., Joo, H., & Kim, H. S. (2022). Development of Simple Method for Flood Control Capacity Estimation of Dam in South Korea. Water, 14(9), 1366. https://doi.org/10.3390/w14091366

Kim, J., Lee, M., Han, H., Kim, D., Bae, Y., & Kim, H. S. (2022). Case study: Development of the CNN model considering teleconnection for spatial downscaling of precipitation in a climate change scenario. Sustainability, 14(8), 4719. https://doi.org/10.3390/su14084719

Kim, D., Han, H., Wang, W., Kang, Y., Lee, H., & Kim, H. S. (2022). Application of deep learning models and network method for comprehensive air-quality index prediction. Applied Sciences, 12(13), 6699. https://doi.org/10.3390/app12136699

Han, H., Kim, D., & Kim, H. S. (2022). Inundation analysis of coastal urban area under climate change scenarios. Water, 14(7), 1159. https://doi.org/10.3390/w14071159

Kim, D., Han, H., Wang, W., & Kim, H. S. (2022). Improvement of deep learning models for river water level prediction using complex network method. Water, 14(3), 466. https://doi.org/10.3390/w14030466

Kim, J., Kim, D., Lee, M., Han, H., & Kim, H. S. (2022). Determining the risk level of heavy rain damage by region in South Korea. Water, 14(2), 219. https://doi.org/10.3390/w14020219

Kim, J., & Han, H. (2021). Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea. Atmospheric Research, 258, 105650. https://doi.org/10.1016/j.atmosres.2021.105650

Han, H., Choi, C., Kim, J., Morrison, R. R., Jung, J., & Kim, H. S. (2021). Multiple-depth soil moisture estimates using artificial neural network and long short-term memory models. Water, 13(18), 2584. https://doi.org/10.3390/w13182584

Jung, J., Han, H., Kim, K., & Kim, H. S. (2021). Machine learning-based small hydropower potential prediction under climate change. Energies, 14(12), 3643. https://doi.org/10.3390/en14123643

Han, H., Choi, C., Jung, J., & Kim, H. S. (2021). Application of sequence to sequence learning based LSTM model (LSTM-s2s) for forecasting dam inflow. Journal of Korea Water Resources Association, 54(3), 157-166. https://doi.org/10.3741/JKWRA.2021.54.3.157

Han, H., Choi, C., Jung, J., & Kim, H. S. (2021). Deep learning with long short term memory based sequence-to-sequence model for rainfall-runoff simulation. Water, 13(4), 437. https://doi.org/10.3390/w13040437

Han, D., Kim, J., Choi, C., Han, H., Necesito, I. V., & Kim, H. S. (2021). Case study: On hydrological function improvement for an endemic plant habitat in Gangcheon wetland, Korea. Ecological Engineering, 160, 106028. https://doi.org/10.1016/j.ecoleng.2020.106028

Kim, J., Han, H., Kim, B., Chen, H., & Lee, J. H. (2020). Use of a high-resolution-satellite-based precipitation product in mapping continental-scale rainfall erosivity: A case study of the United States. Catena, 193, 104602. https://doi.org/10.1016/j.catena.2020.104602

Kim, J., Read, L., Johnson, L. E., Gochis, D., Cifelli, R., & Han, H. (2020). An experiment on reservoir representation schemes to improve hydrologic prediction: Coupling the national water model with the HEC-ResSim. Hydrological Sciences Journal, 65(10), 1652-1666. https://doi.org/10.1080/02626667.2020.1757677

Han, H., Choi, C., Moon, H., Jung, J., Lee, C., & Kim, H. S. (2020). Hydrological impact of Atmospheric River landfall on the Korean Peninsula. Journal of Korea Water Resources Association, 53(11), 1039-1047. https://doi.org/10.3741/JKWRA.2020.53.11.1039

Choi, C., Kim, J., Han, H., Han, D., & Kim, H. S. (2019). Development of water level prediction models using machine learning in wetlands: A case study of Upo wetland in South Korea. Water, 12(1), 93. https://doi.org/10.3390/w12010093

Kim, J., Han, H., Johnson, L. E., Lim, S., & Cifelli, R. (2019). Hybrid machine learning framework for hydrological assessment. Journal of hydrology, 577, 123913. https://doi.org/10.1016/j.jhydrol.2019.123913

Han, H., Kim, J., Chandrasekar, V., Choi, J., & Lim, S. (2019). 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. Atmosphere, 10(8), 466. https://doi.org/10.3390/atmos10080466

Kim, J., Lee, J., Song, Y., Han, H., & Joo, J. (2018). Modeling the runoff reduction effect of low impact development installations in an industrial area, South Korea. Water, 10(8), 967. https://doi.org/10.3390/w10080967