Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany

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@Article{Yadav:2016:Measurement,
  author =       "Basant Yadav and Sudheer Ch and Shashi Mathur and 
                 Jan Adamowski",
  title =        "Discharge forecasting using an Online Sequential
                 Extreme Learning Machine (OS-ELM) model: A case study
                 in Neckar River, Germany",
  journal =      "Measurement",
  volume =       "92",
  pages =        "433--445",
  year =         "2016",
  ISSN =         "0263-2241",
  DOI =          "doi:10.1016/j.measurement.2016.06.042",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0263224116303347",
  abstract =     "Flood forecasting in natural rivers is a complicated
                 procedure because of uncertainties involved in the
                 behaviour of the flood wave movement. This leads to
                 complex problems in hydrological modelling which have
                 been widely solved by soft computing techniques. In
                 real time flood forecasting, data generation is
                 continuous and hence there is a need to update the
                 developed mapping equation frequently which increases
                 the computational burden. In short term flood
                 forecasting where the accuracy of flood peak value and
                 time to peak are critical, frequent model updating is
                 unavoidable. In this paper, we studied a new technique:
                 Online Sequential Extreme Learning Machine (OS-ELM)
                 which is capable of updating the model equation based
                 on new data entry without much increase in
                 computational cost. The OS-ELM was explored for use in
                 flood forecasting on the Neckar River, Germany. The
                 reach was characterized by significant lateral flow
                 that affected the flood wave formation. Hourly data
                 from 1999-2000 at the upstream section of Rottweil were
                 used to forecast flooding at the Oberndorf downstream
                 site with a lead time of 1-6 h. Model performance was
                 assessed by using three evaluation measures: the
                 coefficient of determination (R2), the Nash-Sutcliffe
                 efficiency coefficient (NS) and the root mean squared
                 error (RMSE). The performance of the OS-ELM was
                 comparable to those of other widely used Artificial
                 Intelligence (AI) techniques like support vector
                 machines (SVM), Artificial Neural Networks (ANN) and
                 Genetic Programming (GP). The frequent updating of the
                 model in OS-ELM gave a closer reproduction of flood
                 events and peak values with minimum error compared to
                 SVM, ANN and GP.",
  keywords =     "genetic algorithms, genetic programming, Flood
                 forecasting, Extreme Learning Machine, Artificial
                 intelligence techniques",
}

Genetic Programming entries for Basant Yadav Sudheer Ch Shashi Mathur Jan Adamowski

Citations