Evaluation of reservoir sedimentation using data driven techniques

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@Article{Garg:2013:ASC,
  author =       "Vaibhav Garg and V. Jothiprakash",
  title =        "Evaluation of reservoir sedimentation using data
                 driven techniques",
  journal =      "Applied Soft Computing",
  year =         "2013",
  volume =       "13",
  number =       "8",
  pages =        "3567--3581",
  keywords =     "genetic algorithms, genetic programming, Reservoir
                 sedimentation, Soft computing techniques, Artificial
                 neural networks, Model trees",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2013.04.019",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494613001439",
  abstract =     "The sedimentation is a pervasive complex hydrological
                 process subjected to each and every reservoir in world
                 at different extent. Hydrographic surveys are
                 considered as most accurate method to determine the
                 total volume occupied by sediment and its distribution
                 pattern in a reservoir. But, these surveys are very
                 cumbersome, time consuming and expensive. This complex
                 sedimentation process can also be simulated through the
                 well calibrated numerical models. However, these models
                 generally are data extensive and require large
                 computational time. Generally, the availability of such
                 data is very scarce. Due to large constraints of these
                 methods and models, in the present study, data driven
                 approaches such as artificial neural networks (ANN),
                 model trees (MT) and genetic programming (GP) have been
                 investigated for the estimation of volume of sediment
                 deposition incorporating the parameters influenced it
                 along with conventional multiple linear regression data
                 driven model. The aforementioned data driven models for
                 the estimation of reservoir sediment deposition were
                 initially developed and applied on Gobindsagar
                 Reservoir. In order to generalise the developed
                 methodology, the developed data driven models were also
                 validated for unseen data of Pong Reservoir. The study
                 depicted that the highly nonlinear models ANN and GP
                 captured the trend of sediment deposition better than
                 piecewise linear MT model, even for smaller length
                 datasets.",
}

Genetic Programming entries for Vaibhav Garg V Jothiprakash

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