Kyungdoe ''Doe'' Han

Kyungdoe ''Doe'' Han

Integrated numerical modeler (surface water - groundwater - landscape evolution)

Publications

A Robust, Deep Learning-Based Analysis of Time-Domain Signals for NMR Spectroscopy

Published in Journal of Analytical Science and Technology, 2025

In this study, we demonstrate that neural networks can replace FT in NMR spectroscopy, enabling robust and rapid prediction of spectra and peak lists from FID signals.

Recommended citation: Han et al. (2025). "A Robust, Deep Learning-Based Analysis of Time-Domain Signals for NMR Spectroscopy." Journal of Analytical Science and Technology. Accepted article.
Download Paper

Deciphering the interplay between tectonic and climatic forces on hydrologic connectivity in the evolving landscapes

Published in Frontiers in Water, 2024

The study focuses on phenomena such as interbasin river breakthroughs and knickpoint generation, assembling a time-series of connectivity metrics based on stream network characteristics such as flow rate, flow distance, and captured drainage areas.

Recommended citation: Han, K., & Wilson, J. L. (2024). Deciphering the interplay between tectonic and climatic forces on hydrologic connectivity in the evolving landscapes. Frontiers in Water, 6, 1255883.
Download Paper

Autonomous generation of alluvial fans in landscape evolution models

Published in Earth Surface Processes and Landforms, 2023

We develop a robust and simple rule-based algorithm to autonomously simulate alluvial fan deposition and evolution under continuously developing landscape conditions.

Recommended citation: Han, K., Wilson, J. L., & Emry, E. (2023). Autonomous generation of alluvial fans in landscape evolution models. Earth Surface Processes and Landforms, 48(14), 2842-2863.
Download Paper