Suspended sediment modelling using genetic programming and soft computing techniques

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

@Article{Kisi201248,
  author =       "Ozgur Kisi and Ali Hosseinzadeh Dailr and 
                 Mesut Cimen and Jalal Shiri",
  title =        "Suspended sediment modelling using genetic programming
                 and soft computing techniques",
  journal =      "Journal of Hydrology",
  volume =       "450-451",
  pages =        "48--58",
  year =         "2012",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2012.05.031",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0022169412004076",
  keywords =     "genetic algorithms, genetic programming, Sediment
                 load, Artificial intelligence, Estimating, Sensitivity
                 analysis",
  abstract =     "Modelling suspended sediment load is an important
                 factor in water resources engineering as it crucially
                 affects the design and management of water resources
                 structures. In this study the genetic programming (GP)
                 technique was applied for estimating the daily
                 suspended sediment load in two stations in Cumberland
                 River in U.S. Daily flow and sediment data from 1972 to
                 1989 were used to train and test the applied genetic
                 programming models. The effect of various GP operators
                 on sediment load estimation was investigated. The
                 optimal fitness function, operator functions, linking
                 function and learning algorithm were obtained for
                 modelling daily suspended sediment. The GP estimates
                 were compared with those of the Adaptive Neuro-Fuzzy
                 Inference System (ANFIS), Artificial Neural Networks
                 (ANNs) and Support Vector Machine (SVM) results, in
                 term of coefficient of determination, mean absolute
                 error, coefficient of residual mass and variance
                 accounted for. The comparison results indicated that
                 the GP is superior to the ANFIS, ANN and SVM models in
                 estimating daily suspended sediment load.",
}

Genetic Programming entries for Ozgur Kisi Ali Hosseinzadeh Dailr Mesut Cimen Jalal Shiri

Citations