SPONSOR:
U.S. Army Engineer Research and Development Center (through ORISE Faculty Fellowship)
PROJECT PERIOD:
10/2018 – Present
ABSTRACT:
Surf-zone and riverine bathymetry is of crucial importance for military and shipping operations, and flood management. We developed novel data assimilation and uncertainty quantification methods and software for bathymetry estimation using indirect surface velocity and wave celerity.
Project Publications:
Brodie, K.L., A. Collins, T.J. Hesser, M.W. Farthing, A.S. Bak, and J. Lee. 2020. Augmenting wave-kinematics algorithms with machine learning to enable rapid littoral mapping and surf-zone state characterization from imagery. In Proc. SPIE 11413, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, 1141313.
Ghorbanidehno, H., J. Lee, M. Farthing, T. Hesser, P.K. Kitanidis, and E.F. Darve. (in press). Deep learning technique for fast inference of large-scale riverine bathymetry. Advances in Water Resources
Ghorbanidehno, H., J. Lee, M. Farthing, T. Hesser, P.K. Kitanidis, and E.F. Darve. 2019. Novel data assimilation for nearshore bathymetry. Journal of Atmospheric and Oceanic Technology 36(4): 699–715.
Lee, J., H. Ghorbanidehno, M. Farthing, T. Hesser, E.F. Darve, and P.K. Kitanidis. 2018. Riverine bathymetry imaging with indirect observations. Water Resources Research 54(5): 3704–3727.
PRINCIPAL INVESTIGATOR
