A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Wang2009294,
  author =       "Wen-Chuan Wang and Kwok-Wing Chau and 
                 Chun-Tian Cheng and Lin Qiu",
  title =        "A comparison of performance of several artificial
                 intelligence methods for forecasting monthly discharge
                 time series",
  journal =      "Journal of Hydrology",
  volume =       "374",
  number =       "3-4",
  pages =        "294--306",
  year =         "2009",
  ISSN =         "0022-1694",
  DOI =          "doi:10.1016/j.jhydrol.2009.06.019",
  URL =          "http://www.sciencedirect.com/science/article/B6V6C-4WK48G6-1/2/7cf0d9cf0adb10d24201878b9773ca27",
  keywords =     "genetic algorithms, genetic programming, Monthly
                 discharge time series forecasting, ARMA, ANN, ANFIS,
                 GP, SVM",
  abstract =     "Developing a hydrological forecasting model based on
                 past records is crucial to effective hydropower
                 reservoir management and scheduling. Traditionally,
                 time series analysis and modeling is used for building
                 mathematical models to generate hydrologic records in
                 hydrology and water resources. Artificial intelligence
                 (AI), as a branch of computer science, is capable of
                 analyzing long-series and large-scale hydrological
                 data. In recent years, it is one of front issues to
                 apply AI technology to the hydrological forecasting
                 modeling. In this paper, autoregressive moving-average
                 (ARMA) models, artificial neural networks (ANNs)
                 approaches, adaptive neural-based fuzzy inference
                 system (ANFIS) techniques, genetic programming (GP)
                 models and support vector machine (SVM) method are
                 examined using the long-term observations of monthly
                 river flow discharges. The four quantitative standard
                 statistical performance evaluation measures, the
                 coefficient of correlation (R), Nash-Sutcliffe
                 efficiency coefficient (E), root mean squared error
                 (RMSE), mean absolute percentage error (MAPE), are
                 employed to evaluate the performances of various models
                 developed. Two case study river sites are also provided
                 to illustrate their respective performances. The
                 results indicate that the best performance can be
                 obtained by ANFIS, GP and SVM, in terms of different
                 evaluation criteria during the training and validation
                 phases.",
}

Genetic Programming entries for Wen-Chuan Wang Kwok-Wing Chau Chun-Tian Cheng Lin Qiu

Citations