Genetic Programming for the Downscaling of Extreme Rainfall Events on the East Coast of Peninsular Malaysia

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{oai:doaj.org/article:d32ae083df124d06bca10ce089b34981,
  author =       "Sahar Hadi Pour and Sobri Bin Harun and 
                 Shamsuddin Shahid",
  title =        "Genetic Programming for the Downscaling of Extreme
                 Rainfall Events on the East Coast of Peninsular
                 Malaysia",
  journal =      "Atmosphere",
  publisher =    "Multidisciplinary Digital Publishing Institute",
  year =         "2014",
  volume =       "5",
  number =       "4",
  pages =        "914--936",
  keywords =     "genetic algorithms, genetic programming, downscaling,
                 extreme rainfall indices, statistical downscaling
                 model",
  ISSN =         "2073-4433",
  bibsource =    "OAI-PMH server at doaj.org",
  identifier =   "2073-4433; 10.3390/atmos5040914",
  language =     "English",
  oai =          "oai:doaj.org/article:d32ae083df124d06bca10ce089b34981",
  rights =       "CC BY",
  URL =          "http://www.mdpi.com/2073-4433/5/4/914",
  DOI =          "doi:10.3390/atmos5040914",
  abstract =     "A genetic programming (GP)-based logistic regression
                 method is proposed in the present study for the
                 downscaling of extreme rainfall indices on the east
                 coast of Peninsular Malaysia, which is considered one
                 of the zones in Malaysia most vulnerable to climate
                 change. A National Centre for Environmental Prediction
                 reanalysis dataset at 42 grid points surrounding the
                 study area was used to select the predictors. GP models
                 were developed for the downscaling of three extreme
                 rainfall indices: days with larger than or equal to the
                 90th percentile of rainfall during the north-east
                 monsoon; consecutive wet days; and consecutive dry days
                 in a year. Daily rainfall data for the time periods
                 1961--1990 and 1991--2000 were used for the calibration
                 and validation of models, respectively. The results are
                 compared with those obtained using the multilayer
                 perceptron neural network (ANN) and linear
                 regression-based statistical downscaling model (SDSM).
                 It was found that models derived using GP can predict
                 both annual and seasonal extreme rainfall indices more
                 accurately compared to ANN and SDSM.",
}

Genetic Programming entries for Sahar Hadipour Sobri bin Harun Shamsuddin Shahid

Citations