9 citations · 11 across the 5 of their papers we have counts for
5 papers
Blending Bathymetry: Combination of image-derived parametric approximations and celerity data sets for nearshore bathymetry estimation
Jonghyun Lee, Katherine DeVore, Tyler Hesser +4
Estimation of nearshore bathymetry is important for accurate prediction of nearshore wave conditions. However, direct data collection is expensive and time-consuming while accurate…
Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land s…
Deep learning-based fast solver of the shallow water equations
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for t…
Application of deep learning to large scale riverine flow velocity estimation
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used fo…
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry
Yizhou Qian, Mojtaba Forghani, Jonghyun Harry Lee +4
Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. H…