Abstract
Summary Long-term simulation of reservoir sedimentation suffers from process complexity, lack of data, as well as high computational cost. This work presents an efficient data-driven modeling approach to simulate the spatio-temporal dynamics of bed-evolution reservoirs using principal components regression. The daily bed-evolution of a validated numerical model was used as an input. The first four principal components contributed to some 90% of the total variance of bed-evolution. Multiple linear regression between the eigenvectors of the first four principal components with the inflow discharge, suspended sediment concentration, and differential discharge was able to reconstruct the spatio-temporal bed-evolution. Predictions with similar initial morphological condition performed reasonably. The work is a step forward to advance the assimilation of numerical and data-driven approaches in modeling long-term sedimentation of reservoirs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
