A principal component regression approach to simulate the bed-evolution of reservoirs

作者：Gurmessa T Kebede^*; Bardossy A

来源：Journal of Hydrology, 2009, 368(1-4): 30-41.

DOI：10.1016/j.jhydrol.2009.01.033

• Reservoir sedimentation
• Spatio-temporal bed-evolution
• Numerical simulation
• Multivariate regression
• Principal components regression

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.

• 出版日期2009-4-30