Urmia Lake level forecasting using Brain Emotional Learning (BEL)

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Reza {Mahdi Hadi} and Saeid Shokri and Peyman Ayubi",
  booktitle =    "3th International eConference on Computer and
                 Knowledge Engineering (ICCKE 2013)",
  title =        "{Urmia Lake} level forecasting using Brain Emotional
                 Learning (BEL)",
  year =         "2013",
  month =        oct,
  pages =        "246--251",
  keywords =     "genetic algorithms, genetic programming, brain
                 emotional learning, forecasting, water level, time
  DOI =          "doi:10.1109/ICCKE.2013.6682804",
  abstract =     "This paper has tried to focus on a new approach for
                 water level forecasting of Urmia Lake by using records
                 of past time series and emotional learning. Water level
                 forecasting is important in water resources engineering
                 and management and efficient management of water
                 resources for use. During the past two decades, the
                 approaches artificial intelligence based on the Genetic
                 Programming (GP), Artificial Neural Networks (ANN),
                 fuzzy logic, neuro-fuzzy and statistical method for
                 example ARIMA and recently, chaos theory have been
                 developed. Time series the measurements from tide gauge
                 at Urmia Lake, were used to train emotional learning
                 approach for the period from March 1965 to February
                 2011. The research indicates that there is a non-linear
                 and complex relationship between water input and
                 variables, therefore anticipation seems to be more
                 difficult to implement it with conventional tools of
                 time series prediction. Simulation results prove that
                 the applied method has prominent capability in
                 forecasting time series. In this paper, various
                 criterion including Mean Absolute Error (MAE), Mean
                 Absolute Percentage Error (MAPE), Root Mean Squared
                 Error (RMSE) have been used.",
  notes =        "Also known as \cite{6682804}",

Genetic Programming entries for Reza Mahdi Hadi Saeid Shokri Peyman Ayubi