Exploiting Two Intelligent Models to Predict Water Level: A field study of Urmia lake, Iran

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Shahab Kavehkar and Mohammad Ali Ghorbani and 
                 Valeriy Khokhlov and Afshin Ashrafzadeh and Sabereh Darbandi",
  title =        "Exploiting Two Intelligent Models to Predict Water
                 Level: A field study of Urmia lake, Iran",
  journal =      "International Science Index",
  year =         "2011",
  volume =       "5",
  number =       "3",
  pages =        "731--735",
  keywords =     "genetic algorithms, genetic programming, water-level
                 variation, forecasting, artificial neural networks,
                 comparative analysis.",
  ISSN =         "1307-6892",
  publisher =    "World Academy of Science, Engineering and Technology",
  bibsource =    "http://waset.org/Publications",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://www.waset.org/journals/waset/v51/v51-164.pdf",
  URL =          "http://waset.org/publications/15288",
  URL =          "http://waset.org/Publications?p=51",
  size =         "5 pages",
  abstract =     "Water level forecasting using records of past time
                 series is of importance in water resources engineering
                 and management. For example, water level affects
                 groundwater tables in low-lying coastal areas, as well
                 as hydrological regimes of some coastal rivers. Then, a
                 reliable prediction of sea-level variations is required
                 in coastal engineering and hydrologic studies. During
                 the past two decades, the approaches based on the
                 Genetic Programming (GP) and Artificial Neural Networks
                 (ANN) were developed. In the present study, the GP is
                 used to forecast daily water level variations for a set
                 of time intervals using observed water levels. The
                 measurements from a single tide gauge at Urmia Lake,
                 Northwest Iran, were used to train and validate the GP
                 approach for the period from January 1997 to July 2008.
                 Statistics, the root mean square error and correlation
                 coefficient, are used to verify model by comparing with
                 a corresponding outputs from Artificial Neural Network
                 model. The results show that both these artificial
                 intelligence methodologies are satisfactory and can be
                 considered as alternatives to the conventional harmonic
  notes =        "International Science Index 51, 2011",

Genetic Programming entries for Shahab Kavehkar Mohammad Ali Ghorbani Valeriy Khokhlov Afshin Ashrafzadeh Sabereh Darbandi