Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP)

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Hashmi20111639,
  author =       "Muhammad Z. Hashmi and Asaad Y. Shamseldin and 
                 Bruce W. Melville",
  title =        "Statistical downscaling of watershed precipitation
                 using Gene Expression Programming (GEP)",
  journal =      "Environmental Modelling \& Software",
  volume =       "26",
  number =       "12",
  pages =        "1639--1646",
  year =         "2011",
  ISSN =         "1364-8152",
  DOI =          "doi:10.1016/j.envsoft.2011.07.007",
  URL =          "http://www.sciencedirect.com/science/article/pii/S136481521100168X",
  keywords =     "genetic algorithms, genetic programming, Statistical
                 downscaling, Gene expression programming, Data-driven,
                 Watershed, Precipitation",
  abstract =     "Investigation of hydrological impacts of climate
                 change at the regional scale requires the use of a
                 downscaling technique. Significant progress has already
                 been made in the development of new statistical
                 downscaling techniques. Statistical downscaling
                 techniques involve the development of relationships
                 between the large scale climatic parameters and local
                 variables. When the local parameter is precipitation,
                 these relationships are often very complex and may not
                 be handled efficiently using linear regression. For
                 this reason, a number of non-linear regression
                 techniques and the use of Artificial Neural Networks
                 (ANNs) was introduced. But due to the complexity and
                 issues related to finding a global solution using
                 ANN-based techniques, the Genetic Programming (GP)
                 based techniques have surfaced as a potential better
                 alternative. Compared to ANNs, GP based techniques can
                 provide simpler and more efficient solutions but they
                 have been rarely used for precipitation downscaling.
                 This paper presents the results of statistical
                 downscaling of precipitation data from the Clutha
                 Watershed in New Zealand using a non-linear regression
                 model developed by the authors using Gene Expression
                 Programming (GEP), a variant of GP. The results show
                 that GEP-based downscaling models can offer very simple
                 and efficient solutions in the case of precipitation
                 downscaling.",
}

Genetic Programming entries for Muhammad Z Hashmi Asaad Y Shamseldin Bruce W Melville

Citations