Spatial mapping of pan evaporation using linear genetic programming

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

@Article{Chaudhari:2015:IJHST,
  author =       "Narhari Chaudhari and Shreenivas Londhe and 
                 Kanchan Khare",
  title =        "Spatial mapping of pan evaporation using linear
                 genetic programming",
  journal =      "Int. J. of Hydrology Science and Technology",
  publisher =    "Inderscience Publishers",
  year =         "2015",
  month =        mar # "~07",
  volume =       "4",
  number =       "3",
  pages =        "234--244",
  ISSN =         "2042-7816",
  keywords =     "genetic algorithms, genetic programming,
                 evaporimeters, linear genetic programming, LGP, pan
                 evaporation, spatial mapping, hydrology science, water
                 resources, water management, India",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  URL =          "http://www.inderscience.com/link.php?id=67731",
  DOI =          "DOI:10.1504/IJHST.2014.067731",
  abstract =     "Daily pan evaporation is of utmost importance in
                 planning and managing water resources. The present
                 paper involves estimation of daily pan evaporation at a
                 particular climatic station using daily pan
                 evaporations of surrounding ten climatic stations
                 covering six districts of Maharashtra state (India)
                 with variation in elevations and weather. The
                 surrounding stations were added one by one based on the
                 correlation of each station with the output station.
                 The soft computing technique of linear genetic
                 programming was employed for this spatial mapping
                 exercise. The models were developed for each station as
                 output station (total 11) with the remaining stations
                 (1 to 10) as inputs added one by one. In all 110 LGP
                 models were developed to examine the ability of linear
                 genetic programming to work as virtual pan as and when
                 existing evaporimeters become inoperative. The best LGP
                 model was for Suksale station with coefficient of
                 correlation (r = 0.94) between observed and estimated
                 pan evaporation. This will retrieve the missing
                 evaporation data at one location using data at other
                 locations.",
  notes =        "Kanchan Khare is a Professor of Civil Engineering at
                 the Symbiosis Institute of Technology, Pune, India.",
}

Genetic Programming entries for Narhari Chaudhari S N Londhe K C Khare

Citations