Modeling the Time Variation of Reservoir Trap Efficiency

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@Article{Garg:2010:JHE,

• author = "Vaibhav Garg and V. Jothiprakash",
• title = "Modeling the Time Variation of Reservoir Trap Efficiency",
• journal = "Journal of Hydrologic Engineering",
• year = "2010",
• volume = "15",
• number = "12",
• pages = "1001--1015",
• month = dec,
• email = "vaibhavgarg@iitb.ac.in",
• keywords = "genetic algorithms, genetic programming, Sedimentation, Reservoirs, Hydrologic models, Computation, Artificial intelligence, Neural networks, ANN, India, Evolutionary computation",
• publisher = "American Society of Civil Engineers ASCE",
• ISSN = "1084-0699",
• URL = "http://dx.doi.org/10.1061/(ASCE)HE.1943-5584.0000273",
• URL = "http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0000273",
• URL = "http://ascelibrary.org/doi/pdf/10.1061/(ASCE)HE.1943-5584.0000273",
• DOI = "doi:10.1061/(ASCE)HE.1943-5584.0000273",
• size = "15 pages",
• abstract = "All reservoirs are subjected to sediment inflow and deposition to a certain extent resulting in reduction of their capacity. Trap efficiency (Te), 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 Te with time. In the present study, an attempt has been made to incorporate the age of the reservoir to estimate the Te. This study investigates the suitability of conventional empirical approaches along with soft computing data-driven techniques to estimate the reservoir Te. The incorporation of reservoir age, in empirical model, has resulted in a better Te estimation. Further, to estimate Te 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 Te of a large reservoir.",
• notes = "http://ascelibrary.org/journal/jhyeff

  Also known as \cite{doi:10.1061/(ASCE)HE.1943-5584.0000273}",

}

Genetic Programming entries for Vaibhav Garg V Jothiprakash

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