Modeling the Time Variation of Reservoir Trap Efficiency.

All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency (T_e), a most important parameter for reservoir sedimentation studies, is being estimated using conventional empirical methods till today. A limited research has been carried out on estimating the variation of T_e with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the T_e. This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir T_e. The incorporation of reservoir age, in empirical model, has resulted in a better T_e estimation. Further, to estimate T_e at different time steps, soft computing approaches such as artificial neural networks (ANNs) and genetic programming (GP) have been attempted. Based on correlation analysis, it was found that ANN model (4-4-1) resulted better than conventional empirical methods but inferior to GP. The results show that the GP model is parsimonious and understandable and is well suited to estimate T_e of a large reservoir. [ABSTRACT FROM AUTHOR]