Genetic Programming for Modelling Long-Term Hydrological Time Series

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Wenchuan Wang and Dongmei Xu and Lin Qiu and 
                 Jianqin Ma",
  title =        "Genetic Programming for Modelling Long-Term
                 Hydrological Time Series",
  booktitle =    "Fifth International Conference on Natural Computation,
                 ICNC '09",
  year =         "2009",
  month =        aug,
  volume =       "4",
  pages =        "265--269",
  keywords =     "genetic algorithms, genetic programming, artificial
                 neural network, autocorrelation function, evolutionary
                 computing method, flow prediction method, hydrological
                 time series forecasting, lagged input variable, monthly
                 river flow discharge, reservoir inflow sequence data,
                 root mean square error, transparent-structured system
                 identification, channel flow, correlation methods,
                 forecasting theory, identification, mean square error
                 methods, neural nets, prediction theory, time series",
  DOI =          "doi:10.1109/ICNC.2009.210",
  abstract =     "In recent years, artificial neural networks (ANN) have
                 emerged as a novel identification technique for the
                 forecasting of hydrological time series. However, they
                 represent their knowledge in terms of a weight matrix
                 that is not accessible to human understanding at
                 present. The purpose of this study is to develop a flow
                 prediction method, based on the genetic programming
                 (GP), which is an evolutionary computing method that
                 provides `transparent' and structured system
                 identification. In terms of statistical characteristic
                 of reservoir inflow sequence data, the autocorrelation
                 function is employed to make certain amount of lagged
                 input variables and the root mean square error is
                 adopted as fitness of evaluation. The GP model is
                 examined using the long-term observations of monthly
                 river flow discharges. Through the comparison of its
                 performance with those of the ANN, it is demonstrated
                 that the GP model is an effective algorithm to forecast
                 the long-term discharges.",
  notes =        "Also known as \cite{5366249}",

Genetic Programming entries for Wen-Chuan Wang Dongmei Xu Lin Qiu Jianqin Ma