Comparison of three artificial intelligence techniques for discharge routing

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Rahman Khatibi and Mohammad Ali Ghorbani and 
                 Mahsa Hasanpour Kashani and Ozgur Kisi",
  title =        "Comparison of three artificial intelligence techniques
                 for discharge routing",
  journal =      "Journal of Hydrology",
  year =         "2011",
  volume =       "403",
  number =       "3-4",
  pages =        "201--212",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2011.03.007",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming,
                 Inter-comparison, Model pluralism, Discharge routing,
                 Artificial intelligence modelling, GP, ANFIS, ANN,
  size =         "12 pages",
  abstract =     "The inter-comparison of three artificial intelligence
                 (AI) techniques are presented using the results of
                 river flow/stage timeseries, that are otherwise handled
                 by traditional discharge routing techniques. These
                 models comprise Artificial Neural Network (ANN),
                 Adaptive Nero-Fuzzy Inference System (ANFIS) and
                 Genetic Programming (GP), which are for discharge
                 routing of Kizilirmak River, Turkey. The daily mean
                 river discharge data with a period between 1999 and
                 2003 were used for training and testing the models. The
                 comparison includes both visual and parametric
                 approaches using such statistic as Coefficient of
                 Correlation (CC), Mean Absolute Error (MAE) and Mean
                 Square Relative Error (MSRE), as well as a basic
                 scoring system. Overall, the results indicate that ANN
                 and ANFIS have mixed fortunes in discharge routing, and
                 both have different abilities in capturing and
                 reproducing some of the observed information. However,
                 the performance of GP displays a better edge over the
                 other two modelling approaches in most of the respects.
                 Attention is given to the information contents of
                 recorded timeseries in terms of their peak values and
                 timings, where one performance measure may capture some
                 of the information contents but be ineffective in
                 others. Thus, this makes a case for compiling knowledge
                 base for various modelling techniques.",

Genetic Programming entries for Rahman Khatibi Mohammad Ali Ghorbani Mahsa Hasanpour Kashani Ozgur Kisi