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

Citations