Personal profile
In Korean
이기하 교수(과학기술대학 건설방재공학과)
Education
o (2003) B.S., Chungnambuk National University
o (2005) M.S., Chungnam National University
o (2008) Ph.D., Kyoto University
o (2005) M.S., Chungnam National University
o (2008) Ph.D., Kyoto University
Professional Experience
o (2020~Present)Head of BK21 Four Research Center: Young Engineer education for Multidisciplinary Smart Disaster Management
o (2023~Present) Professor, Kyungpook National University
o (2018~2023) Associate Professor, Kyungpook National University
o (2013~2018) Assistant Professor, Kyungpook National University
o (2012~2013) Legislative Researcher, National Assembly Research Service
o (2009~2012) Researcher, International Water Resources Institute, Chungnam National University
o (2008~2009) Postdoctoral Researcher,DPRI, Kyoto University
o (2023~Present) Professor, Kyungpook National University
o (2018~2023) Associate Professor, Kyungpook National University
o (2013~2018) Assistant Professor, Kyungpook National University
o (2012~2013) Legislative Researcher, National Assembly Research Service
o (2009~2012) Researcher, International Water Resources Institute, Chungnam National University
o (2008~2009) Postdoctoral Researcher,DPRI, Kyoto University
Research Interests
Rainfall-Runoff Modeling, Landslide Assessment, Flood Modeling, Deep Learning Applications, Soil Erosion Modeling, Water Disaster Management, Hydrological Modeling
Major Research Achievements
o Improving Rainfall-Runoff Modeling in the Mekong River Basin using Bias-corrected Satellite Precipitation Products by Convolutional Neural Networks
o Exploring the power of physics-informed neural networks for accurate and efficient solutions to 1D shallow water equations
o Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea
o Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study
o Exploring the power of physics-informed neural networks for accurate and efficient solutions to 1D shallow water equations
o Machine learning for high-resolution landslide susceptibility mapping: case study in Inje County, South Korea
o Deep neural network-based discharge prediction for upstream hydrological stations: a comparative study
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Convolutional neural networks-driven bias correction of satellite precipitation improves rainfall-runoff-inundation modeling
Huong, O. S., Le, X. H., Van, L. N., Lee, G. & Sok, T., Mar 2026, In: International Soil and Water Conservation Research. 14, 1, 100571.Research output: Contribution to journal › Article › peer-review
Open Access2 Scopus citations -
Toward real-time high-resolution fluvial flood forecasting: A robust surrogate approach based on overland flow models
Nguyen, G. V., Van, C. P., Tran, V. N., Van, L. N. & Lee, G., 1 Jan 2026, In: Environmental Modelling and Software. 195, 106716.Research output: Contribution to journal › Article › peer-review
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Advancing parameter uncertainty quantification in hydrology models through integration of variational inference with a differentiable hydrology framework
V. Nguyen, G., Pham Van, C., Nguyen Van, L., Ngoc Tran, V., Kim, Y. & Lee, G., Jul 2025, In: Stochastic Environmental Research and Risk Assessment. 39, 7, p. 2743-2768 26 p.Research output: Contribution to journal › Article › peer-review
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Development and application of the soil organic carbon model considering physical hydrological processes
Yeon, M., Lee, G. & Woo, D. K., 2025, In: Journal of Korea Water Resources Association. 58, 7, p. 553-567 15 p.Research output: Contribution to journal › Article › peer-review
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Investigating the Relationship Between Topographic Variables and Wildfire Burn Severity
Van, L. N. & Lee, G., Sep 2025, In: Geographies. 5, 3, 47.Research output: Contribution to journal › Article › peer-review
Open Access6 Scopus citations