Modeling catchment sediment yield: a genetic programming approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Garg:2014:NH,
  author =       "Vaibhav Garg",
  title =        "Modeling catchment sediment yield: a genetic
                 programming approach",
  journal =      "Natural Hazards",
  year =         "2014",
  volume =       "70",
  number =       "1",
  pages =        "39--50",
  month =        jan,
  email =        "vaibhav@iirs.gov.in",
  keywords =     "genetic algorithms, genetic programming, Sediment
                 yield, Modelling and simulation, Evolutionary
                 technique, Soft computing",
  publisher =    "Springer",
  ISSN =         "0921-030X",
  language =     "English",
  URL =          "http://link.springer.com/article/10.1007%2Fs11069-011-0014-3#page-1",
  DOI =          "doi:10.1007/s11069-011-0014-3",
  size =         "12 pages",
  abstract =     "Hydrologic processes are complex, non-linear, and
                 distributed within a watershed both spatially and
                 temporally. One such complex pervasive process is soil
                 erosion. This problem is usually approached directly by
                 considering the sediment yield. Most of the hydrologic
                 models developed and used earlier in sediment yield
                 modelling were lumped and had no provision for
                 including spatial and temporal variability of the
                 terrain and climate attributes. This study investigates
                 the suitability of a recent evolutionary technique,
                 genetic programming (GP), in estimating sediment yield
                 considering various meteorological and geographic
                 features of a basin. The Arno River basin in Italy,
                 which is prone to frequent floods, has been chosen as
                 case study to demonstrate the GP approach. The results
                 of the present study show that GP can efficiently
                 capture the trend of sediment yield, even with a small
                 set of data. The major advantage of the GP analysis is
                 that it generates simple parsimonious expression
                 offering some possible interpretations to the
                 underlying process.",
}

Genetic Programming entries for Vaibhav Garg

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