Suspended sediment concentration estimation by stacking the genetic programming and neuro-fuzzy predictions

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@Article{Shamaei:2016:ASC,
  author =       "Ehsan Shamaei and Marjan Kaedi",
  title =        "Suspended sediment concentration estimation by
                 stacking the genetic programming and neuro-fuzzy
                 predictions",
  journal =      "Applied Soft Computing",
  volume =       "45",
  pages =        "187--196",
  year =         "2016",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2016.03.009",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494616301181",
  abstract =     "In the new decade due to rich and dense water
                 resources, it is vital to have an accurate and reliable
                 sediment prediction and incorrect estimation of
                 sediment rate has a huge negative effect on supplying
                 drinking and agricultural water. For this reason, many
                 studies have been conducted in order to improve the
                 accuracy of prediction. In a wide range of these
                 studies, various soft computing techniques have been
                 used to predict the sediment. It is expected that
                 combining the predictions obtained by these soft
                 computing techniques can improve the prediction
                 accuracy. Stacking method is a powerful machine
                 learning technique to combine the prediction results of
                 other methods intelligently through a meta-model based
                 on cross validation. However, to the best of our
                 knowledge, the stacking method has not been used to
                 predict sediment or other hydrological parameters, so
                 far. This study introduces stacking method to predict
                 the suspended sediment. For this purpose, linear
                 genetic programming and neuro-fuzzy methods are applied
                 as two successful soft computing methods to predict the
                 suspended sediment. Then, the accuracy of prediction is
                 increased by combining their results with the
                 meta-model of neural network based on cross validation.
                 To evaluate the proposed method, two stations including
                 Rio Valenciano and Quebrada Blanca, in the USA were
                 selected as case studies and streamflow and suspended
                 sediment concentration were defined as inputs to
                 predict the daily suspended sediment. The obtained
                 results demonstrated that the stacking method greatly
                 improved RMSE and R 2 statistics for both stations
                 compared to use of linear genetic programming or
                 neuro-fuzzy solitarily.",
  keywords =     "genetic algorithms, genetic programming, Suspended
                 sediment prediction, Stacking method, Neuro-fuzzy,
                 Neural networks",
}

Genetic Programming entries for Ehsan Shamaei Marjan Kaedi

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