Developing 3D River Channel Modeling with UAV-Based Point Cloud Data

Abstract

Highlights What are the main findings? K-nearest neighbor local regression (KLR) reconstructs UAV-based 3D river channels more accurately than LOWESS, with lower errors and better shape preservation across tests and field sites. KLR handles uneven point density and missing data well, keeping small bed features without over-smoothing. What is the implication of the main finding? Cross-section delineation and hydraulic modeling for flood risk assessment can be done more accurately. Digital-twin river models built from UAV point clouds can be developed more reliably.Highlights What are the main findings? K-nearest neighbor local regression (KLR) reconstructs UAV-based 3D river channels more accurately than LOWESS, with lower errors and better shape preservation across tests and field sites. KLR handles uneven point density and missing data well, keeping small bed features without over-smoothing. What is the implication of the main finding? Cross-section delineation and hydraulic modeling for flood risk assessment can be done more accurately. Digital-twin river models built from UAV point clouds can be developed more reliably.Abstract Accurate characterization of river channel geometry is essential for hydrological and hydraulic analyses, yet the increasing use of unmanned aerial vehicle (UAV) photogrammetry introduces challenges related to uneven point density, shadow-induced data gaps, and spurious outliers. This study proposed a novel approach for reconstructing 3D river channels from UAV-derived point clouds, emphasizing K-nearest neighbor local regression (KLR), and compared it with the LOWESS model. Method performance was examined through controlled simulations of trapezoidal, triangular, and U-shaped synthetic channels, where KLR consistently preserved morphological fidelity and produced lower RMSE than LOWESS, particularly at channel bends and bed undulations, while a neighborhood selection heuristic approach demonstrated robust results across varying data densities. Synthetic channel experiments show that the proposed K-nearest-neighbor local linear regression (KLR) method achieves RMSE values below 0.06 all tested geometries. In contrast, LOWESS produces substantially larger errors, with RMSE values exceeding 0.9 across all channel shapes. Subsequent application to two South Korean field sites reinforced these findings. In the data-scarce Migok-cheon stream, KLR effectively interpolated missing surfaces while maintaining geomorphic realism, whereas LOWESS generated over-smoothed representations. Within the dense Ogsan Bridge dataset, KLR retained small-scale bed features critical for hydraulic simulations and cross-sectional delineation, while LOWESS obscured local variability. Conclusively, the results demonstrate that KLR provides a more reliable and computationally efficient framework for UAV-based 3D river channel reconstruction, with clear implications for hydraulic modeling, flood risk management, and the advancement of digital-twin systems in operational hydrology.