Reservoir Sedimentation Estimation Using Genetic Programming Technique

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Vaibhav Garg and V. Jothiprakash",
  title =        "Reservoir Sedimentation Estimation Using Genetic
                 Programming Technique",
  booktitle =    "World Environmental and Water Resources Congress",
  year =         "2009",
  editor =       "Steve Starrett",
  pages =        "1505--1513",
  month =        "17-21 " # may,
  address =      "Kansas City, Missouri, USA",
  publisher_address = "USA",
  organisation = "Environmental and Water Resources Institute, EWRI",
  publisher =    "ASCE",
  keywords =     "genetic algorithms, genetic programming, Reservoirs,
                 Sediment, India",
  isbn13 =       "9780784410363",
  DOI =          "doi:10.1061/41036(342)149",
  size =         "9 pages",
  abstract =     "To a certain extent, all reservoirs are subjected to
                 the problem of sediment deposition universally.
                 Depending on the amount of material deposited the
                 shortening of reservoir capacity and useful life result
                 in several unpredictable consequences. To determine the
                 total quantity of deposition, as well as the pattern
                 and distribution of deposits in a reservoir,
                 hydrographic survey is the only direct measurement
                 method. These hydrographic survey methods are being
                 considered as expensive, time consuming and cumbersome.
                 In the present study, an attempt has been made to
                 employ genetic programming (GP) soft computing
                 technique to estimate the volume of sediment retained
                 (Sv) in the Pong Reservoir, India. It was found that GP
                 model captured the trend and magnitude of Sv very well.
                 Moreover, GP model provided input-output relationship
                 in the form of computer programs which may be easily
                 used by end user. Also, GP can be effectively used to
                 capture the non-linear relationship between the input
                 and output with short length of data",
  notes =        "Stock No. 41036. Great Rivers

                 Department of Civil Engineering, Indian Institute of
                 Technology Bombay, Powai,Mumbai — 400 076,
                 Maharashtra, India",

Genetic Programming entries for Vaibhav Garg V Jothiprakash